Automated priming and library loading device

ABSTRACT

Provided herein are automated apparatus and methods for the identification of microorganisms in various samples. The disclosure solves existing challenges encountered in identifying and distinguishing various types of microorganisms, including viruses and bacteria in a timely, efficient, and automated manner by library preparation and sequencing.

CROSS-REFERENCE

This application is a continuation of U.S. patent application Ser. No.17/700,456, filed Mar. 21, 2022, which is a continuation of U.S. patentapplication Ser. No. 17/193,710, filed Mar. 5, 2021, now U.S. Pat. No.11,282,587; which is a continuation U.S. patent application Ser. No.16/855,535, filed Apr. 22, 2020; which is a continuation of PCTApplication No. PCT/US18/67750, filed Dec. 27, 2018; which claimspriority to Provisional Patent Application No. 62/611,846, filed Dec.29, 2017; Provisional Patent Application No. 62/646,135, filed Mar. 21,2018; and Provisional Patent Application Ser. No. 62/730,288, filed Sep.12, 2018; each of which is incorporated herein by reference in itsentirety.

BACKGROUND

Food producers recall their products from the marketplace when theproducts are mislabeled or when the food may present a health hazard toconsumers because the food is contaminated or has caused a foodborneillness outbreak. Although these producers rely on several existingmonitoring programs for pathogens, natural toxins, pesticides, and othercontaminants about 48 million cases of foodborne illness are stillidentified annually in the United States alone—the equivalent ofsickening 1 in 6 Americans each year. And each year these illnessesresult in an estimated 128,000 hospitalizations and 3,000 deaths. Thethreats are numerous and varied, with symptoms ranging from relativelymild discomfort to very serious, life-threatening illness. While thevery young, the elderly, and persons with weakened immune systems are atgreatest risk of serious consequences from most foodborne illnesses,some of the microorganisms detected in foods pose grave threats to allpersons.

SUMMARY

In some aspects the disclosure provides a method comprising: (a)deploying an assay to one or more food processing facilities; (b)performing a sequencing reaction of a food sample or of an environmentalsample from said one or more food processing facilities; (c)transmitting an electronic communication comprising a data setassociated with said sequencing reaction of said food sample or of saidenvironmental sample from said one or more food processing facilities toa server; and (d) scanning, by a computer, at least a fraction of saidtransmitted data set for one or more polymorphic regions associated witha microorganism.

In some aspects the disclosure provides a method comprising: (a)obtaining a plurality of nucleic acid sequences from a sample; (b)scanning, by a computer, at least a fraction of said plurality of saidnucleic acid sequences for a plurality of nucleic acid regions from oneor more microorganisms selected from the group consisting of: amicroorganism of the Salmonella genus, a microorganism of theCampylobacter genus, a microorganism of the Listeria genus, and amicroorganism of the Escherichia genus, wherein said scanningcharacterizes said one or more microorganisms with greater than 99.5%sensitivity.

In some aspects the disclosure provides a method comprising: (a)sequencing a plurality of nucleic acid sequences from a food sample orfrom an environmental sample associated with said food sample for aperiod of time; and (b) performing an assay on said food sample or saidenvironment associated with said food sample if said sequencing for saidperiod of time identifies a threshold level of nucleic acid sequencesfrom a microorganism in said food sample.

In some aspects the disclosure provides a method comprising: (a)obtaining a first plurality of nucleic acid sequences from a firstsample of a food processing facility; (b) creating a data file in acomputer that associates one or more of said first plurality of nucleicacid sequences with said food processing facility; (c) obtaining asecond plurality of nucleic acid sequences from a second sample of saidfood processing facility; and (d) scanning a plurality of sequences fromsaid second plurality of nucleic acid sequences for one or moresequences associated with said food processing facility in (b).

In some aspects, the disclosure provides a method comprising: (a)obtaining a first sample of a food processing facility; (b) sequencingsaid first sample of said food processing facility, thereby generating afirst set of sequencing data from said food processing facility; (c)obtaining a second sample of said food processing facility; (d)sequencing said second sample of said food processing facility, therebygenerating a second set of sequencing data from said food processingfacility; and (e) comparing said second set of sequencing data to saidfirst set of sequencing data; and (d) decontaminating said foodprocessing facility if said comparing identifies a pathogenicmicroorganism in said food processing facility.

In some aspects, the disclosure provides a method comprising: (a)obtaining a first plurality of nucleic acid sequences from a firstsample of a food processing facility; (b) obtaining a second pluralityof nucleic acid sequences from a second food sample of said foodprocessing facility; and (c) performing sequence alignments in acomputer between said first plurality of nucleic acid sequences and saidsecond plurality of nucleic acid sequences thereby determining asimilarity between said first sample and said second sample from saidfood processing facility.

In some aspects the disclosure provides a method comprising: (a) addinga reagent to a plurality of nucleic acid molecules from a food sample orfrom an environmental sample associated with said food sample, therebyforming a modified plurality of nucleic acid molecules, whereby saidreagent: (i) modifies a structure of or interacts with a plurality ofnucleic acid molecules derived from one or more dead microorganisms; and(ii) does not modify a structure of a nucleic acid molecule derived fromone or more live microorganisms; thereby providing a modified pluralityof nucleic acid molecules; and (b) sequencing by a sequencing reactionsaid modified plurality of nucleic acid molecules, therebydistinguishing one or more live organisms from said food sample or fromsaid environmental sample associated with said food sample.

In some aspects the disclosure provides a method comprising performing apore sequencing reaction on a plurality of nucleic acid molecules from afood sample or from an environmental sample associated with said foodsample, whereby said pore sequencing reaction distinguishes one or morenucleic acid molecules derived from a dead microorganism from one ormore nucleic acid molecules derived from a live microorganism based on amethylation pattern or another epigenetic pattern of said one or morenucleic acid molecules derived from said dead microorganism.

In some aspects the disclosure provides a method comprising: (a)obtaining a plurality of nucleic acid sequences of a food sample or ofan environmental sample from a food processing facility; (b) performinga first assay in said plurality of nucleic acid sequences of said foodsample, whereby said assay predicts a presence or predicts an absence ofa microorganism in said food sample; and (c) determining, based on saidpredicted presence or said predicted absence of said microorganism of(b) whether to perform a second assay, whereby a sensitivity of saidsecond assay is selected to determine a genus, a species, a serotype, asub-serotype, or a strain of said microorganism.

In some aspects, the disclosure provides a method comprising: (a)detecting a presence or an absence of a non-pathogenic microorganism ina sample; (b) predicting, by a computer system, a presence or an absenceof a pathogenic microorganism in said sample based on said presence orsaid absence of said non-pathogenic microorganism.

In some aspects, the disclosure provides a method comprising: (a)detecting a presence or an absence of a microorganism in a sample or ina facility associated with said sample; and (b) predicting, by acomputer system, a risk presented by said facility based on saidpresence or said absence of said microorganism.

In some aspects, the disclosure provides a method comprising: (a) addinga first barcode to a first plurality of nucleic acid sequences from asample, thereby providing a first plurality of barcoded nucleic acidsequences; and (b) performing a first sequencing reaction on said firstplurality of barcoded nucleic acid sequences, wherein said sequencingreaction is performed on a sequencing apparatus comprising a flow cell;(c) adding a second barcode to a second plurality of nucleic acidsequences from a second sample, thereby providing a second plurality ofbarcoded nucleic acid sequences; and (d) performing a second sequencingreaction on said second plurality of barcoded nucleic acid sequences,wherein said second sequencing reaction is performed on said sequencingapparatus comprising said flow cell, thereby reusing said flow cell.

In some aspects, the disclosure provides a nucleic acid sequencingapparatus comprising: (a) a nucleic acid library preparation compartmentcomprising two or more chambers configured to prepare a plurality ofnucleic acids from a sample for a sequencing reaction, wherein saidcompartment is operatively connected to a nucleic acid sequencingchamber; (b) a nucleic acid sequencing chamber, wherein said nucleicacid sequencing chamber comprises: (i) one or more flow cells comprisinga plurality of pores or sequencing cartridges configured for the passageof a nucleic acid strand, wherein two or more of the one or more flowcells are juxtaposed to one another; and (c) an automated platform,wherein said automated platform is programmed to robotically move asample from said nucleic acid library preparation compartment into saidnucleic acid sequencing chamber.

In some aspects, the disclosure provides a method comprising: (a) addinga first molecular index to a first plurality of nucleic acid sequencesfrom a sample, thereby providing a first plurality of indexed nucleicacid sequences; and (b) adding a second molecular index to said firstplurality of nucleic acid sequences from said first sample, therebyproviding a second plurality of indexed nucleic acid sequences; and (c)adding a third molecular index to said first plurality of nucleic acidsequences from said first sample, thereby providing a third plurality ofindexed nucleic acid sequences; (d) performing a sequencing reaction onsaid third plurality of nucleic acid sequences; and (e) demultiplexing,by a computer system, said third plurality of nucleic acid sequencescomprising said first molecular index, said second molecular index, andsaid third molecular index.

In some aspects, the present disclosure describes a device capable ofdetecting and distinguishing microorganisms, including food-bornepathogens. Food-borne pathogens may include any of the numerousorganisms that spread via food consumption, including enterotoxic E.Coli and Salmonella bacteria. These microorganisms can often survive ina wide variety of environments, including food preparation surfaces andfood processing equipment, as well as on food itself. Tracing theorigins and movements of food-borne pathogen outbreaks oftennecessitates detecting one or more microorganisms from a variety ofsample types, including swabs, food samples, and stool samples. Becauseoutbreaks may be tied to a particular strain of a microorganism, e.g. E.coli O157:H7, and because its detection is critical to stopping itsspread, detection must be rapid and accurate.

A food-borne pathogen detection system may be designed for numerouspurposes, including deployable systems that can be moved to anyenvironment, e.g. a farm field, or grounded devices for laboratorysettings where collected samples are brought to the device. In mostcases, it is highly desirable to have a device that is highly automatedto reduce the number of steps that a user must be involved in toincrease the ease of usage and reduce the risk of contamination or othersources of process failure.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 (FIG. 1 ): illustrates the deploying of a sequencing assay 101 toone or more food processing facilities, food testing lab, or any otherdiagnostic lab 102 for performing a sequencing reaction of a food sampleor of an environmental sample from said food processing facilities suchas, for example, soil, water, air, animal product(s), feed, manure, cropproduction, or any sample associated with a manufacturing plant.

FIG. 2 (FIG. 2 ): illustrates a transmission of an electroniccommunication comprising a data set associated with a sequencingreaction from one or more food processing facilities to a server.

FIG. 3 (FIG. 3 ): is a chart illustrating that a redundancy in geneticmarkers decreases a false negative rate of a method of the disclosure.

FIG. 4 (FIG. 4 ): illustrates a process for predictive risk assessmentbased on a detection of a non-pathogenic microorganism.

FIG. 5 (FIG. 5 ): is a heat map illustrating predictive pathogendetection through machine learning.

FIG. 6 (FIG. 6 ): illustrates a process for predicting a shelf-life of afood based on the detection of a microorganism.

FIG. 7 (FIG. 7 ): is a diagram illustrating the tunable resolution ofvarious assays.

FIG. 8 (FIG. 8 ): is a schematic illustrating various serotypes ofvarious microorganisms that can be detected by an analysis of aplurality of nucleic acid sequences as described herein and furthervalidated with a serotyping assay.

FIG. 9 (FIG. 9 ): is a schematic illustrating one process fordistinguishing a live microorganism from a food or from an environmentalsample.

FIG. 10 (FIG. 10 ): illustrates a process for re-using flow cells withdistinct indexes.

FIG. 11 (FIG. 11 ): illustrates an automated sequencing apparatus of thedisclosure.

FIG. 12 (FIG. 12 ): illustrates a sequencing process with no human touchpoints after enrichment.

FIG. 13 (FIG. 13 ): illustrates the PMAxx-induced removal offree-floating DNA.

FIG. 14 (FIG. 14 ): illustrates a priming port in a flow cell.

FIG. 15 (FIG. 15 ): illustrates a dispensing of a loading library on aflow cell.

FIG. 16 (FIG. 16 ): illustrates the simultaneous targeting of multiplepathogens.

FIG. 17 (FIG. 17 ): illustrates the in silico prediction of primersensitivity/specificity.

FIG. 18 (FIG. 18 ): illustrates the reuse of MinION/GridION flow cells.

FIG. 19 (FIG. 19 ): illustrates the number of reads per sample duringreuse of MinION/GridION flow cells.

FIG. 20 (FIG. 20 ): illustrates the performance of the disclosedautomated handling system on samples spiked with 10 different Salmonellaserotypes (Enteritidis, Thyphimurium, I 4_[5]_12:i:, Newport, Javiana,Infantis, Montevideo, Heidelberg, Muenchen).

FIG. 21 (FIG. 21 ): illustrates a principal component analysis tochicken wing chicken data sets.

FIG. 22 (FIG. 22 ): illustrates a principal component analysis to groundchicken data sets.

FIG. 23 (FIG. 23 ): illustrates periodic and nonperiodic barcodedesigns.

FIG. 24 (FIG. 24 ): illustrates a principle component analysis ofListeria sequences identifying clusters of closely related bacteriawhich likely originated from the same source.

FIG. 25 (FIG. 25 ): illustrates an exemplary automatable nanopore flowcell suitable for use with the methods according to this disclosure.

FIG. 26 (FIG. 26 ): illustrates an exemplary automatable nanopore flowcell with an alternative sample input port plug as described herein.

FIG. 27 (FIG. 27 ): illustrates the phase-separation microbialconcentration method described in Example 23.

DETAILED DESCRIPTION

Food safety is a complex issue that has an impact on multiple segmentsof society. Usually a food is considered to be adulterated if itcontains: (1) a poisonous or otherwise harmful substance that is not aninherent natural constituent of the food itself, in an amount that posesa reasonable possibility of injury to health, or (2) a substance that isan inherent natural constituent of the food itself; is not the result ofenvironmental, agricultural, industrial, or other contamination; and ispresent in an amount that ordinarily renders the food injurious tohealth. The first includes, for example, a pathogenic bacterium, fungus,parasite or virus, if the amount present in the food may be injurious tohealth. An example of the second is the tetrodotoxin that occursnaturally in some organs of some types of pufferfish and that ordinarilywill make the fish injurious to health. In either case, foodsadulterated with these agents are generally deemed unfit forconsumption.

Many different disease-causing microorganisms can contaminate foods, andthere are many different foodborne infections. Although our scientificunderstanding of pathogenic microorganisms and their toxins iscontinually advancing, some of the most common microorganisms associatedwith foodborne illnesses include microorganisms of the Salmonella,Campylobacter, Listeria, and Escherichia genus.

Salmonella for example is widely dispersed in nature. It can colonizethe intestinal tracts of vertebrates, including livestock, wildlife,domestic pets, and humans, and may also live in environments such aspond-water sediment. It is spread through the fecal-oral route andthrough contact with contaminated water. (Certain protozoa may act as areservoir for the organism). It may, for example, contaminate poultry,red meats, farm-irrigation water (thereby contaminating produce in thefield), soil and insects, factory equipment, hands, and kitchen surfacesand utensils.

Campylobacter jejuni is estimated to be the third leading bacterialcause of foodborne illness in the U.S. The symptoms this bacteriumcauses generally last from 2 to 10 days and, while the diarrhea(sometimes bloody), vomiting, and cramping are unpleasant, and theyusually go away by themselves in people who are otherwise healthy. Rawpoultry, unpasteurized (“raw”) milk and cheeses made from it, andcontaminated water (for example, unchlorinated water, such as in streamsand ponds) are major sources, but C. jejuni also occurs in other kindsof meats and has been found in seafood and vegetables.

Although the number of people infected by foodborne Listeria iscomparatively small, this bacterium is one of the leading causes ofdeath from foodborne illness. It can cause two forms of disease. One canrange from mild to intense symptoms of nausea, vomiting, aches, fever,and, sometimes, diarrhea, and usually goes away by itself. The other,more deadly, form occurs when the infection spreads through thebloodstream to the nervous system (including the brain), resulting inmeningitis and other potentially fatal problems.

Escherichia microorganisms are also diverse in nature. For instance, atleast four groups of pathogenic Escherichia coli have been identified:a) Enterotoxigenic Escherichia coli (ETEC), b) EnteropathogenicEscherichia coli (EPEC), c) Enterohemorrhagic Escherichia coli (EHEC),and Enteroinvasive Escherichia coli (EIEC). While ETEC is generallyassociated with traveler's diarrhea some members of the EHEC group, suchas E. coli 0157:H7, can cause bloody diarrhea, blood-clotting problems,kidney failure, and death. Thus, it is important to be able not only toidentify individual microorganism, but also to distinguish them.

Provided herein are methods and apparatus for the identification ofpathogenic and non-pathogenic microorganisms in food and environmentalsamples. The disclosure solves existing challenges encountered inidentifying food borne pathogens, including pathogens of the Salmonella,Campylobacter, Listeria, and Escherichia genus in a timely and efficientmanner. The disclosure also provides methods for differentiating atransient versus a resident pathogen, correlating presence ofnon-pathogenic with pathogenic microorganisms, and distinguishing liveversus dead microorganisms by sequencing, amongst others.

As used herein, the term “food processing facility” includes facilitiesthat manufacture, process, pack, or hold food in any location globally.A food processing facility can, for example, determine the location andsource of an outbreak of food-borne illness or a potential bioterrorismincident.

As used herein, the term “food” includes any nutritious substance thatpeople or animals eat or drink, or that plants absorb, in order tomaintain life and growth. Non-limiting examples of foods include redmeat, poultry, fruits, vegetables, fish, pork, seafood, dairy products,eggs, egg shells, raw agricultural commodities for use as food orcomponents of food, canned foods, frozen foods, bakery goods, snackfood, candy (including chewing gum), dietary supplements and dietaryingredients, infant formula, beverages (including alcoholic beveragesand bottled water), animal feeds and pet food, and live food animals.The term “environmental sample,” as used herein, includes all foodcontact substances or items from a food processing facility. The termenvironmental sample includes a surface swab of a food contactsubstance, a surface rinse of a food contact substance, a food storagecontainer, a food handling equipment, a piece of clothing from a subjectin contact with a food processing facility, or another suitable samplefrom a food processing facility. The term “sample” as used herein,generally refers to any sample that can be informative of an environmentor a food, such as a sample that comprises soil, water, water quality,air, animal production, feed, manure, crop production, manufacturingplants, environmental samples or food samples directly. The term“sample” may also refer to other non-food sample, such as samplesderived from a subject, such as comprise blood, plasma, urine, tissue,faces, bone marrow, saliva or cerebrospinal fluid. Such samples may bederived from a hospital or a clinic.

As used herein, the term “subject,” can refer to a human or to anotheranimal. An animal can be a mouse, a rat, a guinea pig, a dog, a cat, ahorse, a rabbit, and various other animals. A subject can be of any age,for example, a subject can be an infant, a toddler, a child, apre-adolescent, an adolescent, an adult, or an elderly individual.

As used herein, the term “disease,” generally refers to conditionsassociated with the presence of a microorganism in a food, e.g.,outbreaks or incidents of foodborne disease.

The term “nucleic acid” or “polynucleotide,” as used herein, refers to apolymeric form of nucleotides of any length, either ribonucleotides ordeoxyribonucleotides. Polynucleotides include sequences ofdeoxyribonucleic acid (DNA), ribonucleic acid (RNA), or DNA copies ofribonucleic acid (cDNA).

The term “polyribonucleotide,” as used herein, generally refers topolynucleotide polymers that comprise ribonucleic acids. The term alsorefers to polynucleotide polymers that comprise chemically modifiedribonucleotides. A polyribonucleotide can be formed of D-ribose sugars,which can be found in nature, and L-ribose sugars, which are not foundin nature.

The term “polypeptides,” as used herein, generally refers to polymerchains comprised of amino acid residue monomers which are joinedtogether through amide bonds (peptide bonds). The amino acids may be theL-optical isomer or the D-optical isomer.

The term “barcode,” as used herein, generally refers to a label, oridentifier, that conveys or is capable of conveying information aboutone or more nucleic acid sequences from a food sample or from anenvironmental sample associated with said food sample. A barcode can bepart of a nucleic acid sequence. A barcode can be independent of anucleic acid sequence. A barcode can be a tag attached to a nucleic acidmolecule. A barcode can have a variety of different formats. Forexample, barcodes can include: polynucleotide barcodes; random nucleicacid and/or amino acid sequences; and synthetic nucleic acid and/oramino acid sequences. A barcode can be added to, for example, a fragmentof a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) samplebefore, during, and/or after sequencing of the sample. Barcodes canallow for identification and/or quantification of individualsequencing-reads. Examples of such barcodes and uses thereof, as may beused with methods, apparatus and systems of the present disclosure, areprovided in U.S. Patent Pub. No. 2016/0239732, which is entirelyincorporated herein by reference. In some instances, as describedherein, a “molecular index” can either be a barcode itself or it can bea building block, i.e., a component or portion of a larger barcode.

The term “sequencing,” as used herein, generally refers to methods andtechnologies for determining the sequence of nucleotide bases in one ormore nucleic acid polymers, i.e., polynucleotides. Sequencing can beperformed by various systems currently available, such as, withoutlimitation, a sequencing system by Illumina®, Pacific Biosciences(PacBio®), Oxford Nanopore®, Genia (Roche) or Life Technologies (IonTorrent®). Alternatively or in addition, sequencing may be performedusing nucleic acid amplification, polymerase chain reaction (PCR) (e.g.,digital PCR, quantitative PCR, or real time PCR), or isothermalamplification. Such systems may provide a plurality of raw datacorresponding to the genetic information associated with a food sampleor an environmental sample. In some examples, such systems providenucleic acid sequences (also “reads” or “sequencing reads” herein). Theterm also refers to epigenetics which is the study of heritable changesin gene function that do not involve changes in the DNA sequence. A readmay include a string of nucleic acid bases corresponding to a sequenceof a nucleic acid molecule that has been sequenced.

Analyzing Sequences Requested by a Customer

Many food poisoning outbreaks have been associated with pathogenicmicroorganisms including pathogens of the Salmonella, Campylobacter,Listeria, and Escherichia genus. Examples of foods that have beenassociated with such outbreaks include milk, cheeses, vegetables, meats(notably beef and poultry), fish, seafood, and many others. Potentialcontamination sources for various pathogens include raw materials, foodworkers, incoming air, water, and food processing environments. Amongthose, post-processing contamination at food-contact surfaces in a foodprocessing facility poses a great threat to product contamination.

There are many challenges in ensuring the safety of our food supply.Some of these challenges include changes in a food processingenvironment that lead to food contamination, such as the introduction ofa new lot of contaminated raw products. Other challenges include changesin food production and supply, which include importing and exportingfoods from different jurisdictions, which may have distinct standards toassess a risk associated with a food. In addition, new and emergingbacteria strains, toxins, and antibiotic resistance may not be detectedby traditional serotyping or PCR methods of detection.

In some aspects, the disclosure provides a method for the identificationof a microorganism associated with a food or with a food processingfacility. In some aspects the method comprises deploying an assay to oneor more food processing facilities; performing a sequencing reaction ofa food sample or of an environmental sample from said one or more foodprocessing facilities; transmitting an electronic communicationcomprising a data set associated with said sequencing reaction of saidfood sample or of said environmental sample from said one or more foodprocessing facilities to a server; and scanning, by a computer, at leasta fraction of said transmitted data set for one or more genes associatedwith a microorganism. In some embodiments, the method comprisesdeploying an assay to one or more food processing facilities; receivingvia a server an electronic communication comprising a data setassociated with a sequencing reaction, wherein the sequencing reactioncharacterizes a food sample or of an environmental sample from said oneor more food processing facilities; and scanning, by a computer, atleast a fraction of said transmitted data set for one or more genesassociated with a microorganism.

In some aspects, the disclosure provides a method for the identificationof a microorganism associated with a food or with a food processingfacility. In some aspects the method comprises receiving an assay at oneor more food processing facilities; performing a sequencing reaction ofa food sample or of an environmental sample from said one or more foodprocessing facilities; transmitting an electronic communicationcomprising a data set associated with said sequencing reaction of saidfood sample or of said environmental sample from said one or more foodprocessing facilities to a server; and scanning, by a computer, at leasta fraction of said transmitted data set for one or more genes associatedwith a microorganism. In some embodiments, the method comprisesreceiving an assay at one or more food processing facilities; receivingvia a server an electronic communication comprising a data setassociated with a sequencing reaction, wherein the sequencing reactioncharacterizes a food sample or of an environmental sample from said oneor more food processing facilities; and scanning, by a computer, atleast a fraction of said transmitted data set for one or more genesassociated with a microorganism.

In some aspects, the disclosure provides a method for the identificationof a microorganism associated with a food or with a food processingfacility. In some aspects the method comprises deploying an assay to oneor more food processing facilities; receiving an electroniccommunication comprising a data set associated with a sequencingreaction of a food sample or an environmental sample from said one ormore food processing facilities to a server; and scanning, by acomputer, at least a fraction of said transmitted data set for one ormore genes associated with a microorganism. In some embodiments, themethod comprises deploying an assay to one or more food processingfacilities; receiving via a server an electronic communicationcomprising a data set associated with a sequencing reaction, wherein thesequencing reaction characterizes a food sample or of an environmentalsample from said one or more food processing facilities; and scanning,by a computer, at least a fraction of said transmitted data set for oneor more genes associated with a microorganism.

In some instances, the scanning scans fewer than 1%, fewer than 0.1%,fewer than 0.001% of said transmitted data set for one or more genesassociated with said microorganism. Said scanning can be performed toidentify a variety of polymorphic gene regions (comprising SNP's,RFLP's, STRs, VNTR's, hypervariable regions, minisatellites,dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats,simple sequence repeats, indels, and insertion elements) associated witha wide diversity of microorganisms. The variety of polymorphic regionsto be searched for can be determined by creating a large database ofsequences from dozens, hundreds and thousands of food and environmentalsamples. For instance, a database of such polymorphic regions can beconstructed by performing sequencing reactions on at least 5,000, atleast 10,000, at least 15,000, at least 20,000, at least 25,000, atleast 30,000, at least 35,000, at least 40,000, at least 45,000, atleast 50,000 different food or environmental samples. The sequencesobtained can be used to compile information in a database that includes:a) the composition of each sample; and b) the presence or absence of avariety of pathogenic and non-pathogenic organisms associated on eachsample. In addition to containing information about various types ofgenus and species, such databases comprise data from polymorphic generegions of a variety of strains that are variants of a single species.For example, a plurality of sequences in the database might correspondto one or more serovars, morphovars, biovars, or other strain specificinformation.

A variety of sequencing techniques, such as a pore sequencing reaction,a next generation sequencing reaction, a shotgun next generationsequencing, or Sanger sequencing can be used to create a collection ofpolymorphic regions. In some instances, said sequencing reaction is apore sequencing reaction and said pore sequencing reaction distinguishesan epigenetic pattern on a nucleic acid from said food sample or fromsaid environmental sample.

In some cases, said microorganism may be pre-selected by a customer. Acustomer can be an individual or an entity, such as one or more foodprocessing facilities. For example, a customer can be a food packagingfacility; a food distribution center; a food storage center; afacilities handling meat, poultry, egg, or another edible product; afarm; a retail food establishment; a fishing vessel; or another type offacility that also manufactures, processes, packs, or holds foods forany period of time.

A customer may pre-select a microorganism of interest to be identifiedwith any of the methods disclosed herein. For example, raw orundercooked ground beef and beef products are vehicles often implicatedin E. coli O157:H7 outbreaks. Produce, including bagged lettuce,spinach, and alfalfa sprouts, are also increasingly being implicated inE. coli O157:H7 outbreaks. A food processing facility producing rawmeats or other produce associated with E. coli O157:H7 may be a customerthat pre-selects E. coli as a microorganism for analysis. A customer maypre-select one or more types of microorganisms for analysis. Amicroorganism can be one or more of types of bacteria, fungus,parasites, protozoa, and viruses.

Non-limiting examples of bacteria that can be pre-selected by a customerand detected with the methods of the disclosure include: bacteria in theEscherichia genus, including enterotoxigenic Escherichia coli (ETEC),enteropathogenic Escherichia coli (EPEC), enterohemorrhagic Escherichiacoli (EHEC), and enteroinvasive Escherichia coli (EIEC); bacteria of theSalmonella genus; bacteria of the Campylobacter genus; bacteria of theListeria genus; bacteria of the Yersinia genus; bacteria of the Shigellagenus; bacteria of the Vibrio genus; bacteria of the Coxiella genus;bacteria of the Mycobacterium genus; bacteria of the Brucella genus;bacteria of the Vibrio genus; bacteria of the Cronobacter genus;bacteria of the Aeromonas genus; bacteria of the Plesiomonas genus;bacteria of the Clostridium genus; bacteria of the Staphylococcus genus;bacteria of the Bacillus genus; bacteria of the Streptococcus genus;bacteria of the Clostridium genus; and bacteria of the Enterococcusgenus.

A microorganism can be a virus. Non-limiting examples of viruses thatcan be pre-selected by a customer and detected with the methods of thedisclosure include: noroviruses, Hepatitis A virus, Hepatitis E virus,rotavirus.

The performing of a sequencing reaction of a food sample or of anenvironmental sample from said one or more food processing facilitiesoften generates a plurality of nucleic acids sequences that containredundant information or information associated with genes that are notfrom a microorganism. In some aspects, the disclosed methods empowerefficient data analysis by facilitating the targeted analysis of asmaller data set. The generated data could be in the range of Kb, Mb,Gb, Tb or more per analyzed sample. In some aspects, said scanning scansfewer than 1/10, fewer than 1/20, fewer than 1/30, fewer than 1/40,fewer than 1/50, fewer than 1/60, fewer than 1/70, fewer than 1/80,fewer than 1/90, fewer than 1/100, fewer than 1/200, fewer than 1/300,fewer than 1/400, fewer than 1/500, fewer than 1/600, fewer than 1/700,fewer than 1/800, fewer than 1/900, fewer than 1/1,000, fewer than1/10,000, or fewer than 1/100,000 of a data set, such as a transmitteddata set for one or more genes associated with a microorganism. In someaspects, said scanning scans at least a fraction of said transmitteddata set for one or more genes associated with two or more, three ormore, four or more, five or more, six or more, seven or more, eight ormore, nine or more, ten or more microorganisms or another suitablenumber. In some instances, said scanning comprises scanning saidtransmitted data set for one or more polymorphic gene regions. In someinstances, said one or more polymorphic regions comprise one or moresingle nucleotide polymorphisms (SNP's), one or more restrictionfragment length polymorphisms (RFLP's), one or more short tandem repeats(STRs), one or more variable number of tandem repeats (VNTR's), one ormore hypervariable regions, one or more minisatellites, one or moredinucleotide repeats, one or more trinucleotide repeats, one or moretetranucleotide repeats, one or more simple sequence repeats, one ormore indel, or one or more insertion elements. In some instances saidone or more polymorphic regions comprise one or more single nucleotidepolymorphisms (SNP's). A data set associated with a sequencing reactionof a food sample or of an environmental sample can be transmitted to aserver and scanned by a computer.

In some cases, a method can detect a microorganism selected from thegroup consisting of: a microorganism of the Salmonella genus, amicroorganism of the Campylobacter genus, a microorganism of theListeria genus, and a microorganism of the Escherichia genus. Thedetected microorganisms may be of any serotype and a scanning, by acomputer, of one or more genes associated with a microorganism maydetect a microorganism independently of its serotype.

In some cases, a sequencing reaction of a food sample, an environmentalsample, or another sample is a pore sequencing reaction, such as anOxford Nanopore® sequencing reaction. In some instances, at least onebarcode is added to one or more nucleic acid polymers derived from afood sample, from an environmental sample, or from another sample priorto performing said sequencing reaction. In some instances, a pluralityof mutually exclusive barcodes are added to a plurality of foodprocessing facilities, thereby creating a barcode identifier that can beassociated with each food processing facility. For instance, a barcodedsequencing read comprising sequences from a pathogenic microorganism canbe associated with a food or processing facility. In some aspects, amethod disclosed herein further comprises creating, in a computer, adata file that associates said at least one barcode with a source ofsaid food sample, of said environmental sample, or of another sample.

In some aspects, the disclosed methods comprise computer systems ordevices utilizing computer systems that are programmed to implementmethods of the disclosure. FIG. 1 illustrates the deploying of asequencing assay 101 to one or more food processing facilities 102, foodtesting lab, or any other diagnostic lab and performing a sequencingreaction of a food sample or of an environmental sample from said one ormore food processing facilities 102. The food processing facility, foodtesting lab, or any other diagnostic lab may have one or more computersystems that can be used to transmit the results of the sequencing readsto a server, either on premise or remotely deployed cloud environment.FIG. 2 illustrates a transmission of an electronic communicationcomprising a data set associated with a sequencing reaction from one ormore food processing facilities, food testing labs, or any otherdiagnostic labs to a server.

The raw sequence data collected from the sequencing reaction includes alarge set of data that includes all individual sequences as well as thequality at each base. From this large data set, the Clear Labsbioinformatics pipeline extracts a final report that is orders ofmagnitudes smaller. The final report (e.g. electronic communication) isessentially limited to the presence or absence of an organism ofinterest, for instance pathogens, and a further classification of theorganism in terms of serotypes, strains, or other subclassifications.The collected data not used in the report comprises the following:

(a) Read quality: The raw sequences include information on the qualityof the sequences per base. The quality scores can be used in a Bayesianmodel where classifications are statistically sensitive to these qualityscores. Furthermore the quality scores can reveal more on possiblerelations that content of samples have with the accuracy of sequencingplatform.

(b) Sequence time: The raw sequences also include information on thetime when the sequence was read by the sequencer. The number ofsequences form the same source as a function of time can reveal a lotmore information than we currently have. In addition, these time data,can be useful in generating reports for all or some of the samplesearlier than it is currently done.

(c) Trimmed portions of sequences: During demultiplexing of thesequences initial and terminal portions of those sequences are trimmed.Those portions include adapters, index barcodes, and primers. The maindata extracted from the trimmed portions, identifies which sample thesequence belonged to. This decision however is influenced by sequencingerrors, and special properties of the involved sequences. Theinformation on accuracy of this decision, and other factors is lost withtrimming. Moreover the quality of these portions can be used as anindicator for the quality of the entire sequence.

(d) Clustering: An important step in the pipeline involves clusteringsequences that are close enough to each other and representing all thesequences within a cluster by a consensus sequence. This reduces thedata significantly and make is easier to classify these sequences.However these differences, even if minute, carry information that getslost with clustering. Clustering with more stringent criteria, or noclustering can lead into higher resolution and perhaps finerclassification.

A computer system 201 can be programmed or otherwise configured toprocess and transmit a data set from a food processing facility, foodtesting labs, or any other diagnostic labs. The computer system 201includes a central processing unit (CPU, also “processor” and “computerprocessor” herein) 204, which can be a single core or multi coreprocessor, or a plurality of processors for parallel processing. Thecomputer system 201 also includes memory or memory location 205 (e.g.,random-access memory, read-only memory, flash memory), electronicstorage unit 206 (e.g., hard disk), communication interface 202 (e.g.,network adapter) for communicating with one or more other systems, suchas for instance transmitting a data set associated with said sequencingreads, and peripheral devices 204, such as cache, other memory, datastorage and/or electronic display adapters. The memory 205, storage unit206, interface 202 and peripheral devices 203 are in communication withthe CPU 204 through a communication bus (solid lines), such as amotherboard. The storage unit 206 can be a data storage unit (or datarepository) for storing data. For instance, in some cases, the datastorage unit 206 can store a plurality of sequencing reads and provide alibrary of sequences associated with one or more strains from one ormore microorganisms associated with a food processing facility, foodtesting labs, or any other diagnostic labs.

The computer system 201 can be operatively coupled to a computer network(“network”) 207 with the aid of the communication interface 202. Thenetwork 207 can be the Internet, an internet and/or extranet, or anintranet and/or extranet that is in communication with the Internet. Thenetwork 207 in some cases is a telecommunication and/or data network.The network 207 can include one or more computer servers, which canenable distributed computing, such as cloud computing. The network 207,in some cases with the aid of the computer system 201, can implement apeer-to-peer network, which may enable devices coupled to the computersystem 201 to behave as a client or a server.

High Sensitivity Detection of Microorganisms

Some families of microorganisms comprise both harmless and highlypathogenic bugs. The Escherichia family of pathogens, for example,comprise lethal and harmless strains of E. coli. Thus it is not onlyrelevant to be able to identify a pathogen in a sample, but it is alsorelevant to be able to characterize it with high sensitivity. In someaspects, the disclosure provides a method comprising obtaining aplurality of nucleic acid sequences from a food sample, from anenvironment associated with said food sample or from another sample,such as non-food derived samples from clinical sources, including blood,plasma, urine, tissue, faces, bone marrow, saliva or cerebrospinal fluidsamples; scanning, by a computer, at least a fraction of said pluralityof said nucleic acid sequences for a plurality of nucleic acid regionsfrom one or more microorganisms selected from the group consisting of: amicroorganism of the Salmonella genus, a microorganism of theCampylobacter genus, a microorganism of the Listeria genus, and amicroorganism of the Escherichia genus, wherein said scanningcharacterizes said one or more microorganisms with greater than 98%sensitivity, greater than 98.5% sensitivity, greater than 99%sensitivity, greater than 99.5% sensitivity, or greater than 99.9%sensitivity. In some aspects, said scanning characterizes said one ormore microorganisms with greater than 98% specificity, greater than98.5% specificity, greater than 99% specificity, greater than 99.5%specificity, or greater than 99.9% specificity. Sensitivity can be ameasure of a microorganism that is correctly identified (e.g. thepercentage of a microorganism that can be correctly identified based onsequencing read analyses). Specificity (also called the true negativerate) measures the proportion of negatives that are correctly identifiedas such (e.g. the percentage of food samples or environmental samplesthat are correctly identified as not having the microorganism therein).In some instances, said method can distinguish a genetic variant orsubtype of a microorganism (e.g., one or more bacterial strains).

In some instances said plurality of nucleic acid sequences comprisecomplementary DNA (cDNA) sequences, ribonucleic acid (RNA) sequences,genomic deoxyribonucleic acid (gDNA) sequences or a mixture of cDNA,RNA, and gDNA sequences. In some instances, the high sensitivity of thedisclosed method, the high specificity of the disclosed method, or both,can be accomplished by scanning said plurality of said nucleic acidsequences for one or more polymorphic gene regions associated with saidmicroorganisms. In some instances, said one or more polymorphic regionsis selected from the group consisting of one or more single nucleotidepolymorphisms (SNP's), one or more restriction fragment lengthpolymorphisms (RFLP's), one or more short tandem repeats (STRs), one ormore variable number of tandem repeats (VNTR's), one or morehypervariable regions, one or more minisatellites, one or moredinucleotide repeats, one or more trinucleotide repeats, one or moretetranucleotide repeats, one or more simple sequence repeats, one ormore indel, or one or more insertion elements. In some instances, saidscanning compares a scanned polymorphism with a library of sequencescomprising sequences from dozens, hundreds, or thousands of uniquestrains of a microorganism. The higher sensitivity is achieved bycomparing the sequence information of the target region that candiscriminate different microorganisms through the lens of SNPs, indelsor other non-universal target specific markers that are only presentwithin the genome of target micromicroorganisms.

In some aspects, an analysis of a redundancy in genetic markersincreases a specificity and sensitivity of a method disclosed herein.FIG. 3 is a chart illustrating that a redundancy in genetic markersdecreases a false negative rate of a method of the disclosure andincreases its sensitivity as compared to PCR based methods. As shown inFIG. 3 , three commercially available q/PCR based pathogen detectionkits revealed that they would not detect all known Salmonella orListeria genomes. 301 illustrates percentages of Salmonella detection byexisting commercial kits. 302 illustrates percentages of Listeriadetection by existing commercial kits.

A scanning of a plurality of nucleic acid regions within said pluralityof nucleic acid sequences can characterize said one or moremicroorganisms with a desired specificity, sensitivity, or both. In someaspects, a scanning of no more than 0.001%, 0.01%, 0.1%, 1%, 5%, 10%,25%, 50%, 90%, 99%, 100% or any number in between of nucleic acidregions within said plurality of nucleic acid sequences characterizessaid one or more microorganisms with greater than 90%, 95%, 98%, 99%,99.9%, 99.99% and 99.999% sensitivity. In some aspects, the method hasfewer than 2%, fewer than 1.5%, fewer than 1.0%, fewer than 0.5%, orfewer than 0.1% of a false positive identification rate. In someaspects, a scanning of no more than 1% of a whole genome cancharacterize said microorganism.

In some instances, the high sensitivity and specificity of the disclosedmethods are independent of a serotype of the microorganism. Forinstance, a scanning of a plurality of nucleic acid regions can identifya microorganism of the Salmonella genus that has a serotype selectedfrom the group consisting of: Enteritidis, Typhimurium, Newport,Javiana, Infantis, Montevideo, Heidelberg, Muenchen, Saintpaul,Oranienburg, Braenderup, Paratyphi B var. L(+) Tartrate+, Agona,Thompson, and Kentucky; a microorganism of the Escherichia genus has aserotype selected from the group consisting of: O103, O111, O121, O145,O26, O45, and O157; a microorganism of the Listeria genus that has aserotype selected from the group consisting of: 2a, 1/2b, 1/2c, 3a, 3b,3c, 4a, 4b, 4ab, 4c, 4d, and 4e; a microorganism of the Campylobactergenus with the C. jejuni, C. lari, or C. coli serotype and others.

A non-pathogenic strain of Citrobacter, namely Citrobacter sedlakii,expresses the Escherichia coli O157:H7 antigen. This is usuallyassociated with a false positive detection of E. coli in a sample.Typically, when Citrobacter is erroneously classified as E. coli, a foodlot may be unnecessarily disposed of and a food processing facility maybe erroneously classified as a contaminated facility. In some aspects,the high sensitivity of the disclosed methods can be used to distinguisha microorganism from the Escherichia genus from a microorganism of theCitrobacter genus. In some instances, the disclosure provides a methodcomprising: scanning, by a computer, a plurality of sequencing readsfrom a food sample or from an environment associated with said foodsample, whereby said scanning distinguishes a microorganism of aCitrobacter genus from a microorganism of an Escherichia genus byidentifying one or more single nucleotide polymorphisms that areassociated with either said Citrobacter genus or said Escherichia genus.Other examples include E. coli O157:H7 assay cross-reacting with E. coliO55 (which is not an STEC). Also some assays deliver false positivesagainst E. coli O104 (which is not an STEC). Citrobacter is also along-understood challenge for the some systems E. coli O157:H7.

In many cases, disease outbreaks require a rapid response, oftenincluding multijurisdictional coordination. In some aspects, thedisclosure provides methods for the rapid identification of amicroorganism from a food sample. In some instances, the disclosureprovides a method for sequencing a plurality of nucleic acid sequencesfrom a food sample, from an environmental sample associated with saidfood sample or from another sample (such as a clinically derived sample)for a period of time; and performing an assay on said food sample orsaid environment associated with said food sample if said sequencing forsaid period of time identifies a threshold level of nucleic acidsequences from a microorganism in said food sample. In some instancessaid period of time is less than 12 hours, less than 6 hours, less than4 hours, less than 2 hours, less than 1 hour, less than 30 minutes, lessthan 20 minutes, less than 15 minutes or another suitable time. FIG. 4is a schematic illustrating a sequencing of a plurality of nucleic acidsequences from a food sample for a period of time and the advantages ofperforming an assay on said food sample if said sequencing for saidperiod of time identifies a threshold level of nucleic acid sequencesfrom a microorganism in said food sample.

Pathogenic Microorganisms

In general, a microorganism that can injure its host, e.g., by competingwith it for metabolic resources, destroying its cells or tissues, orsecreting toxins can be considered a pathogenic microorganism. Examplesof classes of pathogenic microorganisms include viruses, bacteria,mycobacteria, fungi, protozoa, and some helminths. In some aspects, thedisclosure provides methods for detecting one or more microorganismsfrom a food sample or from an environment associated with said foodsample—such as from a table, a floor, a boot cover, an equipment of afood processing facility—or from a food related sample that comprisesoil, water, water quality, air, animal production, feed, manure, cropproduction, manufacturing plants, environmental samples, or non-foodderived samples, such as samples from clinical sources that compriseblood, plasma, urine, tissue, faces, bone marrow, saliva orcerebrospinal fluid by analyzing a plurality of nucleic acid sequencingreads from such samples.

Many pathogenic microorganisms are further subdivided into serotypes,which can differentiate strains by their surface and antigenicproperties. For instance Salmonella species are commonly referred to bytheir serotype names. For example, Salmonella enterica subspeciesenterica is further divided into numerous serotypes, including S.enteritidis and S. typhimurium. In some aspects, the methods of thedisclosure can distinguish between such subspecies of a variety ofSalmonella by analyzing their nucleic acid sequences.

Escherichia coli (E. coli) bacteria normally live in the intestines ofpeople and animals. Many E. coli are harmless and in some aspects are animportant part of a healthy human intestinal tract. However, many E.coli can cause illnesses, including diarrhea or illness outside of theintestinal tract and should be distinguished from less pathogenicstrains. In some aspects, the methods of the disclosure can distinguishbetween various subspecies of a variety of Escherichia bacteria byanalyzing their nucleic acid sequences.

Listeria is a harmful bacterium that can be found in refrigerated,ready-to-eat foods (meat, poultry, seafood, and dairy—unpasteurized milkand milk products or foods made with unpasteurized milk), and produceharvested from soil contaminated with, for example, L. monocytogenes.Many animals can carry this bacterium without appearing ill, whichincreases the challenges in identifying the pathogen derived from a foodsource. In addition, some species of Listeria can grow at refrigeratortemperatures where most other foodborne bacteria do not, another factorthat increases the challenges of identifying Listeria. When eaten,Listeria may cause listeriosis, an illness to which pregnant women andtheir unborn children are very susceptible. In some aspects, the methodsof the disclosure can distinguish between various subspecies of avariety of Listeria bacteria by analyzing their nucleic acid sequences.

Campylobacter jejuni is estimated to be the third leading bacterialcause of foodborne illness in the United States. Raw poultry,unpasteurized (“raw”) milk and cheeses made from it, and contaminatedwater (for example, unchlorinated water, such as in streams and ponds)are major sources of Campylobacter, but it also occurs in other kinds ofmeats and has been found in seafood and vegetables. In some aspects, themethods of the disclosure can distinguish between various subspecies ofa variety of Campylobacter bacteria by analyzing their nucleic acidsequences.

Non-limiting examples of pathogenic microorganisms that can be detectedwith the methods of the disclosure include: pathogenic Escherichia coligroup, including Enterotoxigenic Escherichia coli (ETEC),Enteropathogenic Escherichia coli (EPEC), Enterohemorrhagic Escherichiacoli (EHEC), Enteroinvasive Escherichia coli (EIEC), Salmonella spp.,Campylobacter jejuni, Listeria, Yersinia enterocolitica, Shigella spp.,Vibrio parahaemolyticus, Coxiella burnetii, Mycobacterium bovis,Brucella spp., Vibrio cholera, Vibrio vulnificus, Cronobacter, Aeromonashydrophila and other spp., Plesiomonas shigelloides, Clostridiumperfringens, Clostridium botulinum, Staphylococcus aureus, Bacilluscereus and other Bacillus spp., Listeria monocytogenes, Streptococcusspp., Enterococcus, and others.

Identifying a New Microorganism in an Environment

Disclosed herein are methods and apparatuses that allow the distinctionof a microorganism that has been newly introduced into a food processingfacility or any other environmental setting in which tracking hygiene iscritical, such as a hospital or a clinic. In some instances, residentmicroorganisms reflect a persistent contamination within a location,e.g., a food processing facility or a hospital, that is very differentthan the transient pathogens that are being repeatedly introduced intothe locations. Discriminating resident and transient pathogens providesmore clarity for differentiation of source of contaminations andintervention strategies. This strategy can be used, for example, tomanage contaminations with managing contaminations with Listeriamonocytogensis. For example, Campylobacter is part of the natural gutmicroflora of most food-producing animals, such as chickens, turkeys,swine, cattle, and sheep. Typically, each contaminated poultry carcasscan carry from about 100 to about 100,000 Campylobacter cells. On onehand, given the fact that less than 500 Campylobacter cells can causeinfection, poultry products pose a significant risk for consumers whomishandle fresh or processed poultry during preparation or who undercookit. On another hand, one must be able to distinguish a normal level of aCampylobacter on a food carcass from a Campylobacter overgrowth in asample or from the presence of a new strain of Campylobacter in a foodprocessing facility, environment, or food sample. One must also be ableto identify a new source of contamination in a facility from existingsources. FIG. 4 illustrates a process for predictive risk assessmentbased on a detection of a non-pathogenic microorganism. Briefly, a foodsample, such as a steak sample illustrated as 401 is processed and anassay, such as a nucleic acid sequencing reaction is performed. Ananalysis of a plurality of nucleic acid sequencing reads from 401 may,in some instances, not detect a particular pathogen, such as the E. colipathogen illustrated in this example. Nevertheless, an analysis 403 ofthe microbiome 402 of the food sample 401 may indicate high risk for apresence of a pathogen, such as E. coli. In such instances, the foodsample may be re-sampled and re-processed to confirm the presence of apathogenic microorganism therein.

In some instances, the methods disclosed herein further compriseperforming an additional assay to confirm the presence of the pathogenicmicroorganism in the sample, such as a serotyping assay, a polymerasechain reaction (PCR) assay, an enzyme-linked immunosorbent (ELISA)assay, or an enzyme-linked fluorescent assay (ELFA) assay, restrictionfragment length polymorphisms (RFLP) assay, pulse field gelelectrophoresis (PFGE) assay, multi-locus sequence typing (MLST) assay,targeted DNA sequencing assay, whole genome sequencing (WGS) assay, orshotgun sequencing assay.

In some aspects, the disclosure provides a method comprising obtaining afirst plurality of nucleic acid sequences from a first sample of a foodprocessing facility; creating a data file in a computer that associatesone or more of said first plurality of nucleic acid sequences with saidfood processing facility; obtaining a second plurality of nucleic acidsequences from a second food sample of said food processing facility;and scanning a plurality of sequences from said second plurality ofnucleic acid sequences for one or more sequences associated with saidfood processing facility in the created data file.

One or more data files can be created that associate a microorganismwith a food processing facility. In some instances, a data file canprovide a collection of sequencing reads that can be associated with oneor more strains of a microorganism present in the processing facility.In some cases, more than 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80,90, 100, or 1000 bacterial strains can be associated with one or morefood processing facilities.

Correlating a Presence of a Microorganism with the Risk Associated witha Food Sample

The instance disclosure recognizes that a presence of somenon-pathogenic microorganisms, i.e. indicator microorganisms, can becorrelated with a presence of pathogenic bacteria in food, inenvironmental samples, or another sample. In some aspects the disclosureprovides a method comprising detecting a presence or an absence of anon-pathogenic microorganism in a food sample, an environment associatedwith said food sample, or another sample described herein, by a computersystem, and a presence or an absence of a pathogenic microorganism insaid food sample, environment associated, or another sample based onsaid presence or said absence of said non-pathogenic microorganism. FIG.5 is a heat map illustrating predictive pathogen detection throughmachine learning using associated non-pathogenic microorganisms. Datawas collected from more than 20,000 food samples varying over the foodcategories identified by CODEX, with presentation proportional to theirmarket share. Among those about 950 samples were identified to havepathogens present. The pathogens were detected via Clear Labs sequencingplatform, as well as, with traditional culturing. Via sequencingmultiple regions, the bacteria present in the samples were detected andquantified (relative to each other) at the species level.

The data was supplemented by alpha diversity measures including Shannonentropy, number of observed OTUs, and Faith's phylogenetic diversitymeasure. The quantification of the bacteria in the samples and thesesupplemented measures, provided coordinates for the data points used inthe final classification. The distance between the data points wascomputed as a combination of unifrac distance and the euclidean distancerestricted to the supplemented coordinates.

The data points were split into training and test subsets. We usedstratified 10-fold cross validation to train support vector machinemodel on the training set. The performance of the model was measured onthe previously separated test set. The scores with regard to detectionof some of the pathogens is presented in FIG. 5 .

The coefficients of the support vector machine classifier were used todetermine bacteria that play significance in determining presence orabsence of the pathogens and therefore to provide signatures that can beused independently of the model. This analysis determined a set ofnon-pathogenic microorganisms that had statistically significantcorrelation with the presence of pathogenic organisms, including membersof the genus Enterobacter. Enterobacter asburiae, Enterobacterbugandensis, Enterobacter cancerogenus, Enterobacter cloacae,Enterobacter endosymbiont, Enterobacter hormaechei, Enterobacter kobei,Enterobacter ludwigii, and Enterobacter soli were among the top 9examples of non-pathogenic bacteria associated with our set ofpathogenic bacteria. For example, Yersinia pseudotuberculosis wasassociated with Enterobacter asburiae; Vibrio vulnificus was associatedwith Enterobacter bugandensis, Enterobacter endosymbiont, andEnterobacter soli; Escherichia coli, Salmonella enterica, and Shigellaboydii were associated with Enterobacter cancerogenus, Enterobactercloacae, and Enterobacter hormaechei; Staphylococcus Aureus wasassociated with Enterobacter kobei; and Yersinia pseudotuberculosis wasassociated with Enterobacter asburiae and Enterobacter ludwigii.

Without being limited by theory, a variety of other samples describedherein can be analyzed as described. Briefly, a sample may be screenedwith any one of the methods described herein and a plurality of nucleicacid sequences may be obtained. Numerous sequences within said pluralityof nucleic acid sequences may be correlated by a machine learningalgorithm with a variety of microorganisms. A prediction can then becreated and a visual output of such prediction, such as the illustrateda heat map can be created by detecting statistically significantcorrelations. For instance, a heat map created by a machine learningalgorithm may illustrate a correlation between a presence of E. coli,Salmonella enterica, and Shigella boydii of one or more non-pathogenicmicroorganisms from the Enterobacter genus, such as Enterobactercancerogenus, Enterobacter cloacae, and Enterobacter hormaechei or anyother bacterial genera. In some aspects, a machine learning algorithm,including the machine learning algorithms described herein, can be usedto create such predictions.

A statistical analysis can be performed to identify the topnonpathogenic species/food ingredients associated with the presence ofVibrio/Staphylococcus/Yersinia/Shigella/Salmonella/Escherichia (anillustrative cluster-based representation of such analysis is presentedin FIG. 5 ). This analysis determined a set of non-pathogenicmicroorganisms that had statistically significant correlation with thepresence of pathogenic organisms, including members of the genusEnterobacter. Enterobacter asburiae, Enterobacter bugandensis,Enterobacter cancerogenus, Enterobacter cloacae, Enterobacterendosymbiont, Enterobacter hormaechei, Enterobacter kobei, Enterobacterludwigii, and Enterobacter soli were among the top 9 examples ofnon-pathogenic bacteria associated with our set of pathogenic bacteria.For example, Yersinia pseudotuberculosis was associated withEnterobacter asburiae; Vibrio vulnificus was associated withEnterobacter bugandensis, Enterobacter endosymbiont, and Enterobactersoli; Escherichia coli, Salmonella enterica, and Shigella boydii wereassociated with Enterobacter cancerogenus, Enterobacter cloacae, andEnterobacter hormaechei; Staphylococcus Aureus was associated withEnterobacter kobei; and Yersinia pseudotuberculosis was associated withEnterobacter asburiae and Enterobacter ludwigii.

Food is a chemically complex matrix. Predicting whether, or how fast,microorganisms will grow in a food, or how quickly a food may spoil, isdifficult. For instance, most foods contain sufficient nutrients tosupport microbial growth. Furthermore, there are many additional factorsthat encourage, prevent, or limit growth of microorganisms in foodsincluding pH, temperature, and relative humidity. In some aspects, theinstant disclosure recognizes that a presence of some microorganism,whether or not pathogenic, can be correlated with a sell-by date, i.e.,a spoilage date of a food. In some aspects the disclosure provides amethod comprising: detecting a presence or an absence of a microorganismin a food sample or in an environmental sample from a food processingfacility; and predicting, by a computer system, a risk presented by saidfood sample or by said food processing facility based on said presenceor said absence of said microorganism.

FIG. 6 illustrates a process for predicting a shelf-life of a food basedon machine learning. Briefly, FIG. 6 illustrates a screening of asample, such as a screening of a plurality of nucleic acid sequencingreads. Subsequently, a machine learning algorithm is used to create arisk profile, whereby said risk profile associates a presence of somemicroorganism with a low or a high likelihood of food spoilage, therebypredicting the sell-by date of a food.

A machine learning algorithm can be used to associate any number ofsequencing reads with a presence of microorganism in a food sample, afood related sample, or another sample. Similarly, a machine learningalgorithm may be able to associate any number of sequencing reads with apresence of a pathogenic microorganism, even if the sequence readsthemselves are not from the pathogenic microorganism.Computer-implemented methods for generating a machine learning-basedclassifier in a system may require a number of input datasets in orderfor the classifier to produce highly accurate predictions. Depending onthe microorganism, matrix, and the microorganisms abundance in the reallife samples of the matrix, the data can be in range of 100, 1000,10000, 100000, 1000000, 10000000, 100000000 sequencing reads. A machinelearning algorithm is selected from the group consisting of: a supportvector machine (SVM), a Naïve Bayes classification, a random forest,Logistic regression and a neural network.

Tuning an Assay Resolution

One can tune the resolution for the detection of a microorganism basedon the source of the sample, e.g., food versus surface swab; and thesensitivity of the assay itself, e.g., genus, species, serotype, versusstrain (obtained via whole genome sequencing). FIG. 7 is a diagramillustrating the tunable resolution of various assays. Briefly, one ormore assays can be used sequentially to obtain a desired level ofsensitivity, such as to determine a genus, a species, a serotype, asub-serotype, or a strain of said microorganism. The assays can beidentical or they can be distinct. FIG. 7 illustrates that a sequencingassay can be used to identify a strain or a sub-serotype of amicroorganism whereas a PCR reaction may be able to identify a speciesor, in some cases, a serotype of a particular microorganism.

In some aspects, the disclosure provides a method comprising: obtaininga plurality of nucleic acid sequences of a food sample, of anenvironmental sample or of another non-food derived sample from a foodprocessing facility or another facility; performing a first assay insaid plurality of nucleic acid sequences of said food sample, wherebysaid assay predicts a presence or predicts an absence of a microorganismin said food sample; and determining, based on said predicted presenceor said predicted absence of said microorganism of the first assaywhether to perform a second assay, whereby a sensitivity of said secondassay is selected to determine a genus, a species, a serotype, asub-serotype, or a strain of said microorganism.

There are various approaches for processing nucleic acids from foodsamples or from environmental samples, such as polymerase chain reaction(PCR) and sequencing. In some cases said assay is a sequencing assaythat provides the ability to obtain sequencing-reads in real time, suchas pore sequencing assay. Sequencing can be performed by various systemscurrently available, such as, without limitation, a sequencing system byIllumina®, Pacific Biosciences (PacBio®), Oxford Nanopore®, Genia(Roche) or Life Technologies (Ion Torrent®). Alternatively or inaddition, sequencing may be performed using nucleic acid amplification,polymerase chain reaction (PCR) (e.g., digital PCR, quantitative PCR, orreal time PCR), or isothermal amplification.

Various strategies may be used for amplification. In some cases, thenucleic acid amplification, polymerase chain reaction (PCR) (e.g.,digital PCR, quantitative PCR, or real time PCR), or isothermalamplification involves amplification with fully or partially degenerateprimers. In some cases, the nucleic acid amplification, polymerase chainreaction (PCR) (e.g., digital PCR, quantitative PCR, or real time PCR),or isothermal amplification involves targeted amplification ofparticular gene or genomic regions. In some cases, targetedamplification of particular gene or genomic regions involves targetedamplification of regions containing and/or circumscribing SNPs, RFLPs,STRs, VNTRs, hypervariable regions, mini satellites, dinucleotiderepeats, trinucleotide repeats, tetranucleotide repeats, simple sequencerepeats, indels, and/or insertion elements associated with or variablebetween individual microorganisms or microorganism serotypes. Thetargeted amplification of the particular gene or genomic regions mayinvolve the use of multiple sets of oligonucleotide primers that arepartially or fully complementary to regions flanking the SNPs, RFLPs,STRs, VNTRs, hypervariable regions, mini satellites, dinucleotiderepeats, trinucleotide repeats, tetranucleotide repeats, simple sequencerepeats, indels, and/or insertion elements. In some embodiments, thetargeted amplification uses at least one, 100, 200, 300, 400, 500, 600,700, or 800 pairs of oligonucleotide primers to amplify particular geneor genomic regions from the nucleic acids.

In some cases, the assay is a serotyping assay. The serotyping assay maycomprise an enzyme-linked immunosorbent (ELISA) assay or anenzyme-linked fluorescent assay (ELFA) assay. A serotype or serovar is adistinct variation within a species of bacteria or virus. Thesemicroorganisms can be classified together based on their cell surfaceantigens, allowing the epidemiologic classification of microorganisms tothe sub-species level. A group of serovars with common antigens iscalled a serogroup or sometimes serocomplex. In some aspects, thedisclosure provides methods for performing a sequencing assay on aplurality of nucleic acids derived from a sample and a then performing aserotyping assay on a derivative of said sample. FIG. 8 is a schematicillustrating various serotypes of various microorganisms that can bedetected by an analysis of a plurality of nucleic acid sequences asdescribed herein and further validated with a serotyping assay.

Differentiating Live Versus Dead Microorganisms

Nucleic acid-based targeted analytical methods, such as PCR provide onlylimited information on the activities and physiological states ofmicroorganisms in samples and cannot distinguish viable cells from deadcells. In some aspects, the disclosure provides methods fordistinguishing a live microorganism in a food sample or in anothersample, from a dead microorganism within the same sample. FIG. 9 is aschematic illustrating one process for distinguishing a livemicroorganism from a food or from an environmental sample. Briefly, FIG.9 illustrates than an amount of a microorganism in a sample can beincreased, i.e., enriched 901, by growing the microorganism in a richmedium for a period of time. A reagent, such as a photoreactiveDNA-binding dye, a DNA intercalating reagent, or another suitablereagent may be added to enriched sample 901. Such reagents distinguishlive 902 microorganisms from dead 903 microorganisms by interacting withthe nucleic acid sequence of dead microorganisms only. In some cases,the disclosure contemplates using propidium monoazide or a derivativethereof as a dye. The modified sample can be prepared for a subsequentreaction 904, such as a sequencing reaction 905.

In some instances the disclosure provides a method comprising adding areagent to a plurality of nucleic acid molecules from a food sample, orfood related sample or another sample described herein thereby forming amodified plurality of nucleic acid molecules, whereby said reagent (i)interacts with and modifies a structure of a plurality of nucleic acidmolecules derived from one or more dead microorganisms; and (ii) doesnot interact with or modify a structure of a nucleic acid moleculederived from one or more live microorganisms; thereby providing amodified plurality of nucleic acid molecules; and sequencing saidmodified plurality of nucleic acid molecules, thereby distinguishing oneor more live organisms from said food sample or from another sample.

In other aspects the disclosure provides a method comprising performinga pore sequencing or other DNA sequencing or hybridization assay on aplurality of nucleic acid molecules from a food sample or from anothersample whereby said pore sequencing reaction distinguishes one or morenucleic acid molecules derived from a dead microorganism from one ormore nucleic acid molecules derived from a live microorganism based on amethylation or other epigenetic pattern of said one or more nucleic acidmolecules derived from said dead microorganism.

In some embodiments, epigenetic patterns, such as methylation, can bedetected in DNA derived from food or environmental samples by chemicalor enzymatic selection methods prior to sequencing. Such methodsinclude, but are not limited to, bisulfite sequencing (includingtargeted bisfulfite sequencing, see e.g. Ziller et al. EpigeneticsChromatin. 2016 Dec. 3; 9:55 and Masser et al. J Vis Exp. 2015; (96):52488) and methylation-sensitive restriction digestion (see e.g.Bitinaite et al. U.S. Pat. No. 9,034,597).

In some embodiments, epigenetic patterns can be detected in DNA derivedfrom food or environmental samples by characteristic changes in ioniccurrent during nanopore sequencing (see e.g. Wescoe et al. J Am ChemSoc. 2014 Nov. 26; 136(47):16582-7 and Laszlo et al. Proc Natl Acad SciUSA. 2013 Nov. 19; 110(47):18904-9).

Barcodes

Unique identifiers, such as barcodes, can be added to one or morenucleic acids isolated from a sample from a food processing facility,from a hospital or clinic, or from another sources. Barcodes can be usedto associate a sample with a source; e.g., to associate an environmentalsample with a specific food processing facility or with a particularlocation within said food processing facility. Barcodes can also be usedto identify a processing of a sample, as described in U.S. Patent Pub.No. 2016/0239732, which is entirely incorporated herein by reference.

In some aspects, the disclosure provides a method comprising adding afirst barcode to a first plurality of nucleic acid sequences from asample, thereby providing a first plurality of barcoded nucleic acidsequences; performing a first sequencing reaction on said firstplurality of barcoded nucleic acid sequences, wherein said sequencingreaction is performed on a sequencing apparatus comprising a flow cell;adding a second barcode to a second plurality of nucleic acid sequencesfrom a second sample, thereby providing a second plurality of barcodednucleic acid sequences; and performing a second sequencing reaction onsaid second plurality of barcoded nucleic acid sequences, wherein saidsecond sequencing reaction is performed on said sequencing apparatuscomprising said flow cell, thereby reusing said flow cell. FIG. 10illustrates a process for re-using flow cells with distinct indexes asdescribed herein. As illustrated by FIG. 10 two distinct indexes, 1001and 1002, such as two different barcodes, can be added to differentsamples prior to sequencing 1003. Since a first sample can be associatedwith a first index 1001 and a second sample can be associated with asecond index 1002 this process effectively allows for the re-using of aflow cell. FIG. 18 and FIG. 19 demonstrate the re-use of MinION/GridIONflow cells. Example 21 demonstrates how certain primer design schemes,such as a nonperiodic design, can reduce crosstalk in situations withhigh multiplexing or closely related sequences, as may happen with reuseof flow cells.

One or more barcodes or block of barcodes may be added to a nucleic acidsequence from a food sample or another sample from a food processingfacility, such as a first, a second, a third, or any subsequent sample.In some cases, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, or 20 identical barcodes are added to such samples. In othercases, distinct barcodes are added to such samples. In some cases, 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20distinct barcodes are added to such samples. The serial addition of twoor more barcodes, either identical in sequence or distinct in sequence,can provide an indexing of a sample that is used in its analyses. Thepresence of additional barcode or barcode blocks make the system morerobust against any barcode manufacturing error and can alsosignificantly reduce the chance of cross contamination between barcodes.In some cases, a barcode is added to a nucleic acid sequence comprisingcomplementary DNA (cDNA) sequences, ribonucleic acid (RNA) sequences,genomic deoxyribonucleic acid (gDNA) sequences, or a mixture of cDNA,RNA, and gDNA sequences.

Barcodes can have a variety of lengths. In some instances a barcode isfrom about 3 to about 25 nucleotides in length, from about 3 to about 24nucleotides in length, from about 3 to about 23 nucleotides in length,from about 3 to about 22 nucleotides in length, from about 3 to about 21nucleotides in length, from about 3 to about 20 nucleotides in length,from about 3 to about 19 nucleotides in length, from about 3 to about 18nucleotides in length, from about 3 to about 17 nucleotides in length,from about 3 to about 16 nucleotides in length, from about 3 to about 15nucleotides in length, from about 3 to about 14 nucleotides in length,from about 3 to about 13 nucleotides in length, from about 3 to about 12nucleotides in length, from about 3 to about 11 nucleotides in length,from about 3 to about 10 nucleotides in length, from about 3 to about 9nucleotides in length, from about 3 to about 8 nucleotides in length, orfrom about 3 to about 7 nucleotides in length.

In other instances, a barcode is from about 4 to about 25 nucleotides inlength, from about 4 to about 24 nucleotides in length, from about 4 toabout 23 nucleotides in length, from about 4 to about 22 nucleotides inlength, from about 4 to about 21 nucleotides in length, from about 4 toabout 20 nucleotides in length, from about 4 to about 19 nucleotides inlength, from about 4 to about 18 nucleotides in length, from about 4 toabout 17 nucleotides in length, from about 4 to about 16 nucleotides inlength, from about 4 to about 15 nucleotides in length, from about 4 toabout 14 nucleotides in length, from about 4 to about 13 nucleotides inlength, from about 4 to about 12 nucleotides in length, from about 4 toabout 11 nucleotides in length, from about 4 to about 10 nucleotides inlength, from about 4 to about 9 nucleotides in length, from about 4 toabout 8 nucleotides in length, or from about 4 to about 7 nucleotides inlength.

a barcode is from about 5 to about 25 nucleotides in length, from about5 to about 24 nucleotides in length, from about 5 to about 23nucleotides in length, from about 5 to about 22 nucleotides in length,from about 5 to about 21 nucleotides in length, from about 5 to about 20nucleotides in length, from about 5 to about 19 nucleotides in length,from about 5 to about 18 nucleotides in length, from about 5 to about 17nucleotides in length, from about 5 to about 16 nucleotides in length,from about 5 to about 15 nucleotides in length, from about 5 to about 14nucleotides in length, from about 5 to about 13 nucleotides in length,from about 5 to about 12 nucleotides in length, from about 5 to about 11nucleotides in length, from about 5 to about 10 nucleotides in length,from about 5 to about 9 nucleotides in length, from about 5 to about 8nucleotides in length, or from about 5 to about 7 nucleotides in length.

a barcode is from about 6 to about 25 nucleotides in length, from about6 to about 24 nucleotides in length, from about 6 to about 23nucleotides in length, from about 6 to about 22 nucleotides in length,from about 6 to about 21 nucleotides in length, from about 6 to about 20nucleotides in length, from about 6 to about 19 nucleotides in length,from about 6 to about 18 nucleotides in length, from about 6 to about 17nucleotides in length, from about 6 to about 16 nucleotides in length,from about 6 to about 15 nucleotides in length, from about 6 to about 14nucleotides in length, from about 6 to about 13 nucleotides in length,from about 6 to about 12 nucleotides in length, from about 6 to about 11nucleotides in length, from about 6 to about 10 nucleotides in length,from about 6 to about 9 nucleotides in length, from about 6 to about 8nucleotides in length, or from about 3 to about 7 nucleotides in length.

Apparatus

Automated nucleic acid sequencing apparatuses can provide a robustplatform for the generation of nucleic acid sequencing reads.Unfortunately, many apparatuses have a high rate of failure, i.e., highrate of error of the sequencing reaction itself, which require manualintervention in such instances, such as re-loading of samples into flowcells. In some aspects, the disclosure provides an automated nucleicacid sequencing apparatus that requires no manual intervention in theevent of a failure of a sequencing reaction. In some aspects, thedisclosure provides a nucleic acid sequencing apparatus comprising: anucleic acid library preparation compartment comprising two or morechambers configured to prepare a plurality of nucleic acids for asequencing reaction, wherein said compartment is operatively connectedto a nucleic acid sequencing chamber; a nucleic acid sequencing chamber,wherein said nucleic acid sequencing chamber comprises: (i) one or moreflow cells comprising a plurality of pores configured for the passage ofa nucleic acid strand, wherein said two or more flow cells arejuxtaposed to one another; and an automated platform, wherein saidautomated platform is programmed to robotically move a sample from saidnucleic acid library preparation compartment into said nucleic acidsequencing chamber. FIG. 11 illustrates an automated sequencingapparatus of the disclosure. 1101 is a diagram of the apparatuscomprising the nucleic acid sequencing compartment 1102. Nucleic acidlibrary preparation compartment 1103 shows a variety of chambersconfigured to prepare a plurality of nucleic acids for a sequencingreaction in close proximity to a sequencing chamber 1104, whichcomprises one or more flow cells. Briefly, an automated apparatus of thedisclosure is programmed to move one or more samples from the librarypreparation chambers 1103 into a sequencing chamber 1104 upon detectinga failure in a sequencing reaction. This provides a sequencing processwith no human touch points after a sample is added to the librarypreparation chamber, as illustrated in FIG. 12 . FIG. 12 illustrates anembodiment where a sample from a food processing facility, from ahospital or clinical setting, or from another source can be manuallyprocessed between 6 am to 6 pm or any shorter or longer incubationwindow by incubating the sample in a presence of a growth medium (e.g.,enrichment) and automatically processed after the sample is added to anucleic acid preparation chamber 1103.

The disclosed apparatus is programmed in such a manner that saidautomated platform moves one or more samples from said nucleic acidlibrary preparation compartment into said nucleic acid sequencingchamber. Upon detecting a failure of a sequencing reaction, theautomated platform moves one or more samples from the failed sequencingflow cell or apparatus to the next sequencing flow cell or apparatus. Inmany cases, such samples comprise nucleic acid sequences that includeone or more barcodes. In some cases, a plurality of mutually exclusivebarcodes are added to a plurality of nucleic acids in said two or morechambers of the nucleic acid library preparation compartment 1103,thereby providing a plurality of mutually exclusive barcoded nucleicacids within the apparatus. In some instances, the automated platformrobotically moves two or more of said mutually exclusive barcodednucleic acids into said nucleic acid sequencing chamber, in someinstances by moving said mutually exclusive barcoded nucleic acids intoa same flow cell of said one or more flow cells.

The present disclosure describes an apparatus for the automateddetection of food-borne pathogens via the sequencing of genomiclibraries from samples introduced into the instrument. In some aspects,the apparatus may comprise four main components: library chambers forlibrary preparation, fluid handling systems, sequencing flow cells, andautomation systems. Within the scope of the present disclosure, thereare numerous possible uses of the pathogen detection system.

Library Chambers

The present disclosure describes a device comprising one or more librarychambers. Each library chamber may be capable of a broad range offunctions including, but not limited to, sample preparation, sampleenrichment, nucleic acid amplification, and purging. In some aspects,the library preparation may be performed entirely within a singlelibrary chamber. In other aspects, each library chamber may be reservedfor a separate function in the library preparation process. Dependingupon the processes necessary for library preparation, library chambersmay be operatively connected to each other, and to one or more flowcells, or they may only have operative connections to a sequencing flowcell.

A library chamber may comprise one or more chambers and a securablehatch. The hatch allows access by a user or automated loading system forsample loading. The opening and closing mechanism of the hatch may bemanual or electrically-actuated. In some aspects, a library chamber maycomprise a main compartment and a secondary compartment for pre-loadinga sample. Pre-loading may comprise a process of decontamination or otherprocesses to prevent outside contaminants such as dust and pollen fromentering the apparatus. Following decontamination, the specimen may betransferred into the main compartment. In other aspects, samples may beloaded into a single library chamber and the entire library chamber willbe decontaminated.

The library chambers may be configured to accommodate a broad range ofsamples. When tracing the outbreak and spread of a food-borne pathogen,many possible sample types may be tested, including, but not limited to,soil samples, crop samples, tissue samples, cloth swabs, stool samples,and fluid samples. In some aspects, the present disclosure may provide adynamic platform that is capable of enriching a detectable amount ofnucleic acids from a sample. In some aspects, the library chamber maycomprise a fixed unit of the apparatus described in the presentenclosure and is capable of repeated reuse. In some aspects, a device ofthe disclosure comprises at least 5, at least 10, at least 15, at least20, or another suitable number of library chambers 1103. In otheraspects, the library chamber may comprise a cartridge for samplecollection in the field. In such an embodiment, the library chamberwould be loaded into the sequencing apparatus manually before thecommencement of an automated library preparation and sequencing assay.

For the present disclosure, a library chamber may be configured inmultiple ways depending upon how it will be utilized. A library chambermay comprise one or more inlet ports for the addition of reagents,gases, or any other necessary materials for library preparation. Theinlet ports may be physically positioned at any portion of the librarychamber depending upon the function of the inlet port. In some aspects,a library chamber may comprise one or more inlet ports. In some aspects,a library chamber may comprise 1 to 10 inlet ports, 2 to 10 inlet ports,3 to 10 inlet ports, 4 to 10 inlet ports, 5 to 10 inlet ports, 6 to 10inlet ports, 7 to 10 inlet ports, 8 to 10 inlet ports, or 9 to 10 inletports. In some aspects, an inlet port may be configured for theintroduction of gases, liquids, or solids. In some aspects, an inletport may be positioned near the top of the library chamber for uses suchas the addition of liquid media. In other aspects, an inlet port may bepositioned near the bottom of the library chamber for uses such as gasbubbling or sparging. A library chamber may also comprise one or moreexit ports. In some aspects, a library chamber may comprise one or moreexit ports. In some aspects, a library chamber may comprise 1 to 10 exitports, 2 to 10 exit ports, 3 to 10 exit ports, 4 to 10 exit ports, 5 to10 exit ports, 6 to 10 exit ports, 7 to 10 exit ports, 8 to 10 exitports, or 9 to 10 exit ports. In some aspects, an exit port may beconfigured for the removal of gases, liquids, or solids.

The preparation of nucleic acid libraries may require the enrichment ofa sample by culturing such samples in nutritious media that supports theenrichment of a sufficient amount of nucleic acids to perform asequencing assay. In some aspects, the library chamber may serve toenrich a sample by serving as a cell-culturing chamber. In such aconfiguration, the library chamber may be filled with a cell-growthmedium and any other reagents necessary to promote cell growth. In someaspects, the library chamber may be connected to modules of thermalregulation, including both heating and cooling, to promote optimal cellgrowth. The library chamber may be capable of aerobic or anaerobicoperation. In some aspects, aerobic operation may comprise bubbling orsparging with oxygen or air. In other aspects, anaerobic operation maycomprise bubbling or sparging the library chamber with a non-oxygenatedgas including, but not limited to, nitrogen, helium, carbon dioxide orhydrogen. Mechanical agitation of cell culture may be necessary toprevent sedimentation of cells. In some aspects, agitation may beprovided by sufficient bubbling of gases through the library chamber. Inother aspects, a micro-impeller may provide mechanical mixing to thelibrary chamber. In some aspects, a micro-impeller may comprise animpeller blade connected to a motor through a sealed bearing in asurface of the library chamber. In other aspects, a micro-impeller maycomprise an impeller blade and shaft entirely contained within thelibrary chamber. In such an embodiment, the impeller blade and shaft maycomprise a magnetically-susceptible material such that the operation ofan electromagnet in close proximity to the library surface may inducethe spinning of the blade. In some aspects, a library chamber may beused to lyse cells as a method of freeing the nucleic acids containedwithin the cells. In some aspects, cell lysing and nucleic acid capturemay be performed within one library chamber. In other aspects, celllysing and nucleic acid capture may be performed in successive librarychambers via a series of assays and material transfer between librarychambers.

In some aspects, a library chamber in the present disclosure maycomprise a DNA amplification and manipulation device. The librarychamber may be a platform for any DNA amplification technique,including, but not limited to emulsion PCR. As a PCR platform, thelibrary chamber may include a themocycler. The library chamber may alsocomprise a device for a variety of other DNA manipulation techniquesincluding, but not limited to, restriction assays and ligation assays.In some aspects, the present disclosure may comprise a means to amplifya nucleic acid library. The library chamber may be used to fragmentlarger pieces of genomic DNA and add identifying sequences such asbarcodes to nucleic acid fragments. All DNA manipulations may beperformed in a single library chamber or multiple assays may beperformed in one or more successive library chambers.

A library chamber may be comprised of a variety of materials dependingupon the assays to be performed in it. The library may be comprised ofmaterials such as metal, glass, ceramic or plastic. Library chambers maycomprise metals that are non-magnetic, paramagnetic or ferromagnetic. Inthe present disclosure, library chambers may comprise metals such asaluminum, tungsten, tungsten oxide, austenitic stainless steel, orferritic stainless steel. A library chamber may comprise a thermoplasticor a machinable plastic. The library chamber may comprise a plastic suchas polyethylene, polypropylene, polyester, or polycarbonate. The chambermaterial may be chosen for a variety of properties including, but notlimited to, biocompatibility, corrosion resistance, chemical reactivity,surface energy, electrical capacitance, electrical resistivity,electrical conductivity, magnetic properties, ductility, durability,elasticity, flexibility, hardness, malleability, mass density, tensilestrength, surface roughness, machinability, light absorbance, lighttransmittance, index of refraction, light emissivity, thermal expansion,specific heat, and thermal conductivity. In some aspects, a librarychamber may be composed of a single material that is acceptable for allintended uses. In other aspects, a library chamber may be composed ofmultiple materials, e.g. a glass chamber with metal inserts forconnections to inlet and outlet ports. In some aspects, library chambersurfaces may comprise a chosen material with an applied coating. Suchcoatings may be used for a variety of purposes including, but notlimited to, anti-corrosion, anti-friction, hydrophobicity,hydrophilicity, anti-agglomeration, anti-adsorption, pro-adsorption,anti-fouling, anti-static, chemical reactivity and chemical inertness.In the present disclosure, the library preparation portion of theapparatus may comprise multiple library chambers arranged in parallel orseries configurations for a variety of purposes. In either case, eachlibrary chamber in the apparatus may comprise a different materialdesign specifically chosen for the intended application of the librarychamber.

Library chambers may be designed to include inline detection. Thepurpose of detection systems may include measuring system properties ordetecting failed assays. Detection systems may be used to measure avariety of system properties, including, but not limited to, celldensity, nucleic acid concentration, nucleic acid purity, pH,temperature, pressure, oxygen concentration, fluid density, fluidviscosity, dielectric constant, absorption spectrum, and heat capacity.In some aspects, a library chamber may include an optical port comprisedof an optically-opaque material such as quartz glass. In some aspects,the transmittance, absorption, reflection or refraction of visible,infrared, microwave, or ultraviolet light sources may be measured usingembedded optical ports. In some aspects, the library chambers mayinclude mechanical ports for inserting measurement devices including,but not limited to pH probes, thermocouples, pressure gauges anddielectric probes. In some aspects, library chambers may be designed toallow fluid to be drawn out of fluid inlet or outlet ports formeasurement at downstream instrumentation.

Fluid Handling Systems

In the present disclosure, fluid transfer may occur between one or morelibrary chambers and one or more sequencing flow cells. The apparatusmay comprise systems for ensuring the accurate transfer of fluids. Fluidtransfer may also be involved in many other aspects of device operation,including, but not limited to cell culturing, cell lysis, nucleic acidpurification, nucleic acid amplification, nucleic acid ligation, nucleicacid fragmentation, nucleic acid sequencing, sequence flow cell priming,chamber mixing, chamber cleaning, and chamber purging. The sequencingflow cell, as designed, must maintain a gas-free operational state forits entire life. In many embodiments of the present disclosure, thefluid handling system will be designed to ensure that no gas may betransferred from the library preparation system to the sequencing flowcell system.

Numerous fluids may be involved in the operation of the food-bornepathogen detection described in the present disclosure. Liquids used mayinclude buffers, acids, bases, surfactants, emulsions, suspensions,chelating agents, and solutions. Liquids used may include, but are notlimited to, deionized water, HCl, H₂SO₄, HNO₃, NaOH, KOH, NaCl, KCl,CaCl₂, MgCl₂, EDTA, ethanol, and methanol. Gases used may include inertgases, oxidizing gases and reducing gases. Gases used may include, butare not limited to N₂, air, O₂, He, Ar, H₂, and CO₂. Commonly usedliquids may be stored in the device. In some aspects, liquids may bestored in onboard chambers. In other aspects, liquids may be stored incartridges that can be added or removed manually or via an automatedsystem. In some aspects, gases may be delivered via external tanks orplumbed lines via inlet ports in the apparatus.

Fluids may be moved through a food-borne pathogen detection apparatus bya variety of mechanisms. Fluid movement devices may include pumps,compressors, regulators, blowers, and fans. The apparatus in the presentdisclosure may comprise one or more pumps for liquid transfer. Thesepumps may be responsible for moving fluids into library chambers,emptying library chambers, transferring fluids from library chambers tosequencing flow cells, moving fluids through flow cells, preventingsedimentation of solids, clearing filters and draining waste fluids fromthe apparatus. Depending upon the specific applications, pumps includedin the described apparatus may comprise positive-displacement pumps,peristaltic pumps, gear pumps, rotary pumps, screw pumps, piston pumps,or diaphragm pumps. Pumps and compressors may also be used for gastransfer. Regulators and compressors may be used to adjust gas pressuresin the apparatus. Vacuum pumps may be used to void library chambersduring purging operations. Fluid transfer may also be achieved viapassive mechanisms such as gravity feeding and capillary action. Apathogen or a non-pathogenic microorganism detection device may compriseone or more valves for fluid control. Valves may be located in any flowline, including at inlet and exit ports for library chambers, at inletsand exits to the sequencing flow cells, and at inlet and drainage portsfor the apparatus. Valves may be capable of manual control or automatedcontrol. Fluid transfer may be controlled by devices such as mass flowcontrollers and rotameters. Fluid transfer regulation may be achievedvia manual controls on the apparatus, analog or digital electroniccontrol systems on the apparatus, or via computer systems interfacedwith a remote sequencing apparatus.

For the present disclosure, connectivity between device inlets, librarychambers, sequencing flow cells, and drainage ports may be pursued.Connectivity may be achieved via direct coupling of components atotherwise sealed junctions or junctions that have movable openings.Connectivity between components may be achieved by any suitable method,including, but not limited to, mated flanges, compression fittings,friction fittings, hose barbs, and magnetic couplings. In some aspects,a seal may be needed between two connected components. Depending uponthe design, a seal may need to be air-tight, leak-free, detachable, orpermanent. A seal may comprise a gasket, O-ring, metal compressionfitting or plastic compression fitting. Seals may be chosen from avariety of materials including, but not limited to polypropylene,polycarbonate, rubber, copper, or graphite. Connectivity may alsocomprise piping or tubing between system components. Piping or tubingmay comprise any material suitable to the chosen application. Materialsmay be chosen to for properties including anti-fouling, anti-friction,hydrophobicity, hydrophilicity, durability, flexibility, strength, cost,and biocompatibility. Piping or tubing materials may include, but arenot limited to, stainless steel tubing, copper tubing, aluminum tubing,brass tubing, rigid plastic tubing, or flexible plastic tubing. In someaspects, piping or tubing may be fitted permanently in the device. Inother aspects, piping or tubing lines may be disposable.

Other devices may necessarily be part of the flow system for the devicedescribed in the present disclosure. In some aspects, various flow linesmay be equipped with one or more filters. Filter may be for liquids orgases. Filters may be for various purposes including capturing cells orcellular components, maintaining sterility from outside fluid sources,capturing any particle contaminants, or any other debris that may needto be excluded from the library chambers or sequencing flow cells. Insome aspects, one or more bubblers may be included at the junctionbetween a library chamber and an external gas line. Bubblers maycomprise a fritted metal, fritted glass or any other porous materialthat distributes flowing gas. In some aspects, a separation device maycomprise a connection between units in the described apparatus.Separation devices may be used to perform ultrafiltration, adsorption,reverse osmosis, extraction, chromatography, sedimentation, sieving orvapor-liquid separation. In some aspects, a device may be placed betweena library chamber and a sequencing flow cell to ensure the removal ofall gas bubbles.

In some aspects, the sterility of the sequencing apparatus may bemaintained to promote a higher efficiency operation or a sequencing thatmay be exposed to fewer contaminants. The internal and automatedportions of the device may be enclosed within a sealed housing toprevent the intrusion of any airborne particles and debris such as dust,mold, mildew, pollen, bacteria, viruses and lint. The sealed housing maybe purged of ambient air by use of a vacuum pump or may be held underpositive pressure via an attached source of compressed gas. Certainexternal portions of the device may require frequent exposure to theoutside environment, presenting potential sources of contamination, e.g.library chambers during sample insertion. Any external port, inlet orchamber may be held under positive pressure to minimize the chances ofunwanted debris or biological entities depositing into the system duringoperation. Any and all chambers, cells and fluid transfer systems mayundergo one or more washing, cleaning or purging processes to removecontaminants or residual matter from normal operations. Washing,cleaning or purging may comprise the use of detergents, surfactants,acids, bases, alcohols, deionized water, DNAses, RNAses, proteases,lipases, or any other cleansing method. Washing, cleaning or purging mayinvolve heat treatments or vacuum evacuation. Any and all fluid transfersystems may comprise materials with anti-fouling or biocidal coatings orsurface functionalization to minimize the deposition of contaminants,especially in regions with fluid stagnation. Fluid flow may be laminarto increase residence time in a portion of the apparatus, e.g. aprolonged cleansing step, or may be turbulent to decrease residence timeor decrease mixing, e.g. rapid cell movement to reduce sedimentation.

Automation Systems

The food-borne pathogen detection apparatus described in the presentdisclosure is intended for autonomous or semi-autonomous operation. Insome aspects, the apparatus may only require manual intervention for theinput of samples and reagents, and all further operations may be handledvia an automated software/hardware system. In other aspects, theapparatus may require manual input of information, instructions orphysical materials, such as reagents, at particular times in theinstrument's operations. The device may operate using customizedalgorithms for each operation or may utilize standard algorithms.Algorithms may be manually input via onboard control systems or sentfrom a remote computer system. The device may be hardwired to anexternal computer system or communicate wirelessly. The sequencingapparatus may be capable of exporting data in packets or transmittingdata in real-time as sequencing is performed. In some aspects, theapparatus will automatically detect failed operations including, but notlimited to, failed bacterial enrichment, failed DNA amplification orpurification, and failed sequencing. In some aspects, the system mayinclude diagnostic or analytical devices at inlet or exit ports, inlibrary chambers, or in any flow line to provide data on the status ofongoing operations.

The apparatus in the present disclosure may operate via electricalsupply from an external power supply, e.g. a wall outlet, or run via aself-contained battery system. Field portable versions of the device maybe intended to run in conjunction with portable power systems such assolar panels or portable generators. In some aspects, the apparatus willcomprise all necessary electrical components to accept either DC or ACpower, as the power supply source dictates.

The sequencing device may utilize robotics for automated operation. Insome aspects, robotics may be responsible for any and all internaloperations, including, but not limited to moving fluids, opening andclosing valves, adding reagents, performing cleaning operations,performing and monitoring bacterial growth operations, performing andmonitoring DNA amplification and purification operations, performingsequencing assays, priming or reconditioning flow cells, and dischargingwaste from the apparatus. In some aspects, all components of asequencing device may be fixed in their positions, with robotics usedprimarily to control the movement of liquids, gases and other materialsthrough the system. In other aspects, robotics may be used to movelibrary chambers to a point of direct connectivity with another portionof the system, e.g. a sequencing flow cell.

In some aspects, fluid transfer operations may be mediated by one ormore automated pipette systems. In some aspects, a pipette system maycomprise a single pipette. In other aspects, a pipette system maycomprise an array of pipettes arranged in multiplexed fashion. One ormore pipettes may be capable of dispensing fluids via positivepressure-driven flow or removing fluids via a negative pressuredifferential (vacuum). In some aspects, one or more pipettes may beconfigured to dispense or withdraw fluids individually. In otheraspects, one or more pipettes may be configured to dispense or withdrawfluids simultaneously. Fluids may be dispensed or withdrawn in acontinuous or metered fashion. In some aspects, a metered pipette maydispense or withdraw fluid volumes of about 0.1 μl to about 1000 μl. Insome aspects a metered pipette may dispense or withdraw fluid volumes ofabout 0.1 μl to 10 μl, 0.1 μl to 20 μl, 0.1 μl to 30 μl, 0.1 μl to 40μl, 0.1 μl to 50 μl, 0.1 μl to 60 μl, 0.1 μl to 70 μl, 0.1 μl to 80 μl,0.1 μl to 90 μl, 0.1 μl to 100 μl, 1 μl to 10 μl, 1 μl to 20 μl, 1 μl to30 μl, 1 μl to 40 μl, 1 μl to 50 μl, 1 μl to 60 μl, 1 μl to 70 μl, 1 μlto 80 μl, 1 μl to 90 μl, 1 μl to 100 μl, 10 μl to 20 μl, 10 μl to 30 μl,10 μl to 40 μl, 10 μl to 50 μl, 10 μl to 60 μl, 10 μl to 70 μl, 10 μl to80 μl, 10 μl to 90 μl, 10 μl to 100 μl, 10 μl to 200 μl, 10 μl to 300μl, 10 μl to 400 μl, 10 μl to 500 μl, 10 μl to 1000 μl, 100 μl to 200μl, 100 μl to 300 μl, 100 μl to 400 μl, 100 μl to 500 μl, 100 μl to 1000μl, 250 μl to 500 μl, or about 250 μl to 1000 μl. In some aspects, eachpipette may comprise a separate pressure actuator. In other aspects, twoor more pipettes may be controlled by the same pressure actuator.Pipette tips may be permanent or disposable. In some aspects, disposablepipette tips may comprise a hollow plastic piece that mates to apermanent surface. Disposable pipette tips may be secured to thepermanent surface via downward pressure on the permanent surface ontothe plastic. An automated fluid transfer system may comprise anautomated method for removing disposable pipette tips.

An array of pipettes with pressure actuators or connectivity to pressureactuators may be mounted on an automated translation stage capable ofmovement in one or more dimensions. In some aspects, an automatedtranslation stage may be capable of 3-dimensional movement. In someaspects, an automated translation stage may be capable of rotationalmovement. In some aspects, an automated translation system may becoupled to one or more motors, pneumatic devices, or any other method ofproducing linear motion. Translation may be produced in continuous orincremented, step-wise fashion. Translational movements may be producedon the order of about 1 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100μm, 200 μm, 300 μm, 400 μm, 500 μm, 600 μm, 700 μm, 800 μm, 900 μm, 1mm, 5 mm, 10 mm, 20 mm, 30 mm, 40 mm, 50 mm, 100 mm, 200 mm, 300 mm, 400mm, 500 mm, 600 mm, 700 mm, 800 mm, 900 mm, or 1000 mm.

Automated Priming and Library Loading Device

Oxford Nanopore flow cells have a flow path as shown in FIG. 25 , (whichdepicts a schematic cross-section along the flow path, top, and thecorresponding features on a picture of a flow cell, bottom) and arepre-filled with a conditioning liquid to protect the nanopore membraneduring storage. Further description of such flow cells can be founde.g., in WO2018007819A1. However, the commercial form of the oxfordnanopore flow cell (e.g. GridIONx5™ cell) is not provided in a formwhere all the preparation steps for sequencing can be performed by anautomated process. Particularly, all steps of storage buffer replacementand priming of liquid flow through the flow cell are difficult toautomate because of a flat plastic removable seal that covers the sampleinput port (the presence and absence of which is demonstrated in FIG. 14and FIG. 15 which cannot be conveniently removed by automated processes.

The nanopore flow cell device comprises a sensor provided in a sensingchamber (2505); a flow path comprising a sensing chamber inlet (2509)and a sensing chamber outlet (2504) connecting to the sensing chamberfor respectively passing liquid into and out of the sensing chamber, anda sample input port (2506) in fluid communication with the inlet; and aliquid collection channel (2503) downstream of the outlet. The deviceadditionally has a flow path interruption (2502, e.g. a valve activatedby an actuatable lever accessible from the top surface of the device)between the sensing chamber outlet (2501) and the liquid collectionchannel (2503), preventing liquid from flowing into the liquidcollection channel (2503) from upstream, and the device may be activatedby completing the flow path between the sample input port (2506) and theliquid collection channel (2503), such as by opening a valve when avalve is in place of the flow path interruption (2502). When provided bythe manufacturer as a new flow cell, conditioning liquid fills from thesample input port (2506) to the flow path interruption (2502) such thatthe sensor (within 2505) is covered by liquid and is unexposed to a gasor gas/liquid interface. The device additionally has a buffer input port(2507) in fluid communication with the sensing chamber inlet (2509), aflow path interruption (2502, e.g. a valve activated by an actuatablelever accessible from the top surface of the device, which is the sameas the valve controlling the flow between the sensing chamber outlet andthe liquid collection channel), and a flat plastic removable plugcovering the sample input port (2506).

Before use, the conditioning liquid filling the flow cell must bereplaced by priming buffer suitable for operation of the device, butbuffer must be introduced in such a way that it does not allow thesensor in the sensing chamber (2505) to come in contact with bubbles orgas/liquid interface, which damage the sensor. Thus, the normal methodfor buffer replacement involves the removal of the flow pathobstruction(s) (2506), also known as activation (which allows liquid toflow through the device), followed by buffer introduction into thebuffer input port (2507), which displaces the conditioning liquid withinthe device. The flat plastic plug is then removed from the input port(2506), and priming buffer is applied via the input port so there is acontinuous fluid channel from the input port (2506) through the sensingchamber outlet (2501) that is ready to receive sample. Because there isa continuous fluid channel from the input port (2506) through thesensing chamber outlet (2501), application of one or more volumes oftest liquid to the wet surface of the input port provides a net drivingfor sufficient to introduce the one or more volumes of test liquid intothe device and displace buffer liquid into the liquid collection channel(2503), allowing normal operation of the device (e.g. flow of nucleicacids within the test liquid through the sensor chamber).

Because the flow path interruptions (2506) are provided as a valve witha horizontally-actuatable lever on the surface of the device, opening ofthe valve during the priming process can be automated e.g. via a roboticarm. The removal of the flat sample input port plug is difficult toautomate.

Replacement of the flat sample input port plug with an alternative plugthat is amenable to robotic removal, however, allows the opening of thesample input port to be opened during an automated process. In anexample, the alternative plug is cylindrical, consisting of a first flatend and a second conical tapered end, wherein the conical tapered endtapers to a size sufficient to completely fill and obstruct the sampleinput port. Such a cylindrical plug will project a sufficient distance(e.g. 1-3 cm, or 1.0, 1.5, 2.0, 2.5, or 3 cm) above the surface of theflow cell when used to plug the sample input port via its tapered end sothat it can be conveniently removed by a robotic arm without touchingthe surface of the flow cell. Such an exemplary alternative plug isdepicted in FIG. 26 . In some instances, the alternative plug is a shapethat is not cylindrical (e.g. rectangular, square, triangular), butwhich projects at least a sufficient distance (e.g. 1-3 cm, or 1.0, 1.5,2.0, 2.5, or 3 cm) above the surface of the flow cell when used to plugthe sample input port that it can be removed without disturbing thesurface of the flow cell and at least tapers to a size sufficient toplug the sample input port. In some embodiments, the alternative plug isconstructed of a ferromagnetic material such as ferritic stainlesssteel, so that handling (placement and/or removal) of the plug can beaccomplished with an electromagnet. In some embodiments, the alternativeplug comprises a metallic material. In some embodiments, the alternativeplug comprises tungsten, aluminum, austenic stainless steel, ferriticstainless steel, or another material that is resistant to dilute nitricacid (HNO₃), 1M NaOH, or dilute NaOCl for removal of RNA/DNA/RNAsecontamination. In some embodiments, the alternative plug comprisespolypropylene or polycarbonate.

The automated removal of the alternative plug can be incorporated intothe process of Example 13 to accomplish fully automated nanopore porecell priming and sample loading of one or multiple flow cellssimultaneously. An exemplary automated process involving the use of suchan alternative plug described above involves first replacing the flatplastic sample input plug port (the “SpotON” plug depicted in FIG. 14 )with an embodiment of the alternative plug described above (e.g.manually). The flow path interruptions/valves (2506) of the flow cellare opened, and the device is placed inside an automated sequencingapparatus as described above, in Example 13, or in FIG. 13 . In someembodiments, the flow path interruptions/valves (2506) of the flow cellare opened via an automated process after the flow cell has beenmanually placed in the automated sequencing apparatus. The automatedsequencing apparatus then provides priming buffer to the buffer inputport (2506), such as the buffers described in Example 13, and afterconditioning buffer in the flow cell has been displaced, the alternativeplug is removed (e.g. by a robotic arm) and sample is provided to thesample input port via the automated sequencing apparatus. The sequencingprocess then proceeds as otherwise described in Example 13 withautomated handling and fluid addition. In some embodiments, thealternative plug is replaced after the sequencing run is completed sothat the flow cell can be flushed and re-primed for at least anadditional run. In this way, the nanopore flow cells can be repeatedlyre-primed and reused e.g. for running additional samples on the sameflow cell, or for running repeats of samples where the flow sequencinghas failed, data recording has failed, or the PCR amplification ofnucleic acids derived from the food or environmental sample have failed.In some embodiments, the flow cells in the automated sequencingapparatus are re-primed and reused at least several times (e.g. at least2, 3, 4, 5, 6, 7, 8, 9 or 10 times). Importantly, the alternative plugprovides a way of re-priming the cell on-demand without manualintervention. This is particularly important in the case ofsequencing/amplification/data transmission failure, as it enablesautomated repeating of sample runs after hours or at othertimes/locations where the automated sequencing apparatus is unattended.

Configurations and Methods of Operation

Described herein are methods for operating an automated sequencingapparatus for food-borne pathogen detection. Such a system may becomprised in numerous fashions. In some aspects, the apparatus maycomprise a fixed device with minimal moving parts. In other aspects, thedevice may comprise a dynamic, robotic system with numerous movingparts.

Regardless of configuration, a sequencing operation may comprise thesteps of sample loading, library generation, library transfer,sequencing and data communication. Each step may comprise numerousembodiments. The methods described herein are exemplary and do notconstrain the possible mode of operation for any embodiment of thefood-borne pathogen detection system.

Sample loading may comprise any process for the emplacement of aspecimen in a library chamber. In some aspects, a specimen may bemanually placed into the library chamber. In other aspects a sample maybe loaded by an automated system. In some aspects, samples may becaptured in cartridges that can be loaded into the library chamber by anautomated sample handling system. Sample loading may comprise processesfor decontaminating library chambers of environmental contaminants fromthe loading process.

Library generation may comprise a sequence of assays and methodsdepending upon the methodology of library generation. Assays may includecell culture, cell lysis, DNA amplification, DNA purification, washings,extractions, purifications, dilutions, concentrations, buffer exchanges,restriction assays, barcoding, and any other biochemical methodnecessary to generate a DNA library. In some aspects, a single librarychamber may be utilized for all processing steps. In other aspects, asample may be relayed between multiple chambers for each processing stepwith emptied library chambers undergoing wash procedures to removeexcess reagents.

Sequencing of the DNA library may occur in one or more flow cells. A DNAlibrary may be distributed into multiple libraries to speed theprocessing of a sample. Sequencing may comprise the real-timetransmission of data or staged transmission of packets of data. Dataprocessing may occur in one or more onboard processors in the sequencingapparatus, or may occur at a remote terminal.

Although many details of the operation may be found in all embodimentsof the food-borne pathogen detection system, there may be numerousmethods of configuring the system to achieve the desired level ofperformance and accuracy within an allowable footprint. Theconfiguration may be motivated in response to the application of thesystem. In some aspects, the sequencing apparatus may be field-portablefor rapid deployment in difficult environments such as farm fields orrestaurant kitchens. Such a device may have a limited footprint withroom for few library chambers or sequencing flow cells. In otheraspects, the device may comprise a lab-scale fixture with an effectivelyunconstrained footprint. Such an instrument may comprise hundreds tothousands of library chambers and sequencing flow cells with a roboticsystem for sample management. Described below are several exemplaryembodiments of apparatus configurations. Other embodiments are possiblewithin the scope this disclosure.

Static Operation

A food-borne pathogen detection apparatus may comprise a fixed or staticdevice. In such a configuration, the library chambers may be positionedpermanently relative to the sequencing flow cells. Connectivity betweenlibrary chambers may comprise a system of tubing, pumps and valves. Thefluid flow system would be capable of performing all necessary fluidtransfer operations during operation without manual intervention. Insome aspects, one or more library chambers may comprise a sequencingapparatus. The library chambers may be arranged in a serial or parallelfashion.

A sequencing apparatus may comprise a single library chamber and one ormore sequencing flow cells. In some aspects, the connection between thelibrary chamber and sequencing cell may comprise a flow line and one ormore valves. Such an embodiment may comprise the simplest device withthe most compact footprint.

Two or more library chambers arranged in parallel fashion may comprise asequencing apparatus. In some aspects, a plurality of library chambersmay have connectivity with a single flow cell. In other aspects, eachlibrary may be connected to a single flow cell. In some aspects,parallel operation of library chambers may comprise performing allaspects of sample preparation and library generation within a singlelibrary chamber. Following library generation, nucleic acid from alibrary chamber may be transferred to one or more sequencing flow cells.Multiple flow cells may be used for a single DNA library to speed thesequencing process.

Two or more chambers may also be arranged in a serial fashion. A serialoperation may comprise a staged operation with each library chamberspecialized to perform a specific operation within the devicemethodology. A serial arrangement of library chambers may comprise alarger footprint than a system comprising a single library chamber orparallel library chambers. A serial arrangement of chambers may comprisea more complicated flow system with additional valves and pumps neededto actuate all necessary fluid transfer steps. A serial configurationmay offer more efficient operation because each library chamber isdesigned specifically for its function.

Conveyer Operation

In some aspects, a food-borne pathogen detection apparatus may comprisea series of two or more library chambers on a conveyer system. Theconveyer may comprise a linear or circular system. A sequencingapparatus may comprise one or more conveyer systems. Each conveyer unitmay couple to one or more sequencing flow cells. The purpose of theconveyer system is to move a library chamber into connectivity with asequencing cell when the nucleic acid library has been prepared. Eachlibrary chamber on the conveyer system may have connectivity with thenecessary components to carry out library preparation procedures. Whenlibrary preparation is completed, the library chamber may be moved intoposition by the conveyer and coupled to the sequencing flow cell. Uponcompletion of fluid transfer from the library chamber to the sequencingcell, the conveyer may move the completed library chamber and out andreplace the flow cell plug or place a new library chamber inconnectivity with the flow cell. In some aspects, the conveyer systemcomprises a circular conveyer with four library chambers and two flowcells mounted along an axis. In this configuration, two library chambershave connectivity to flow cells while two library chambers conductlibrary preparation procedures. When new libraries are ready forsequencing, the conveyer may rotate 90° to connect the new librarychambers, while the previously-sequenced chambers being new librarypreparations.

Compartment Operation

A compartment-style sequencing apparatus may comprise a system ofhundreds or thousands of library chambers in a large-footprint device.In some aspects, a library chamber may comprise a cartridge that isloaded with a specimen external to the sequencing apparatus. Thecartridge may be transferred into the apparatus and then moved to adocking station comprising one or more connective ports by a pluralityof robotic conveyances. The docking ports may provide all necessaryfluid transfer operations to complete library preparation within thelibrary chamber. When library preparation is complete, the cartridge maybe transferred to an available sequencing flow cell by a plurality ofrobotic conveyances. In some aspects, a cartridge-style library chambermay be simultaneously connected to fluid transfer ports and a sequencingflow cell to create a semi-robotic system with a reduced footprint.

FIG. 11 illustrates a compartmentalized automated sequencing apparatusof the disclosure with a desktop footprint. 1101 is a diagram of theapparatus comprising the nucleic acid sequencing compartment 1102.Nucleic acid library preparation compartment 1103 shows a variety ofchambers configured to prepare a plurality of nucleic acids for asequencing reaction in close proximity to a sequencing chamber 1104,which comprises one or more flow cells. Briefly, an automated apparatusof the disclosure is programmed to move one or more samples from thelibrary preparation chambers 1103 into a sequencing chamber 1104 upondetecting a failure in a sequencing reaction. This provides a sequencingprocess with no human touch points after a sample is added to thelibrary preparation chamber, as illustrated in FIG. 12 . FIG. 12illustrates an embodiment where a sample from a food processingfacility, from a hospital or clinical setting, or from another sourcecan be manually processed between 6 am to 6 pm or any shorter or longerincubation window by incubating the sample in a presence of a growthmedium (e.g., enrichment) and automatically processed after the sampleis added to a nucleic acid preparation chamber 1103.

The disclosed apparatus is programmed in such a manner that saidautomated platform moves one or more samples from said nucleic acidlibrary preparation compartment into said nucleic acid sequencingchamber. Upon detecting a failure of a sequencing reaction, theautomated platform moves one or more samples from the failed sequencingflow cell or apparatus to the next sequencing flow cell or apparatus. Inmany cases, such samples comprise nucleic acid sequences that includeone or more barcodes. In some cases, a plurality of mutually exclusivebarcodes are added to a plurality of nucleic acids in said two or morechambers of the nucleic acid library preparation compartment 1103,thereby providing a plurality of mutually exclusive barcoded nucleicacids within the apparatus. In some instances, the automated platformrobotically moves two or more of said mutually exclusive barcodednucleic acids into said nucleic acid sequencing chamber, in someinstances by moving said mutually exclusive barcoded nucleic acids intoa same flow cell of said one or more flow cells.

Classification

Microbiome data (data representing the presence or absence of particularspecies or serotypes of microbes as determined by sequencing) of theinvention can be used to classify a sample. For example, a sample can beclassified as, or predicted to be: a) containing a particular pathogenicmicrobe, b) containing a particular serotype of a pathogenic microbe,and/or c) contaminated with at least one species/serotype of pathogenicmicrobe. Many statistical classification techniques are known to thoseof skill in the art. In supervised learning approaches, a group ofsamples from two or more groups (e.g. contaminated with a pathogen andnot) are analyzed with a statistical classification method. Microbepresence/absence data can be used as a classifier that differentiatesbetween the two or more groups. A new sample can then be analyzed sothat the classifier can associate the new sample with one of the two ormore groups. Commonly used supervised classifiers include withoutlimitation the neural network (multi-layer perceptron), support vectormachines, k-nearest neighbours, Gaussian mixture model, Gaussian, naiveBayes, decision tree and radial basis function (RBF) classifiers. Linearclassification methods include Fisher's linear discriminant, logisticregression, naive Bayes classifier, perceptron, and support vectormachines (SVMs). Other classifiers for use with the invention includequadratic classifiers, k-nearest neighbor, boosting, decision trees,random forests, neural networks, pattern recognition, Bayesian networksand Hidden Markov models. One of skill will appreciate that these orother classifiers, including improvements of any of these, arecontemplated within the scope of the invention.

Classification using supervised methods is generally performed by thefollowing methodology:

In order to solve a given problem of supervised learning (e.g. learningto recognize handwriting) one has to consider various steps:

1. Gather a training set. These can include, for example, samples thatare from a food or environment contaminated or not contaminated with aparticular microbe, samples that are contaminated with differentserotypes of the same microbe, samples that are or are not contaminatedwith a combination of different species and serotypes of microbes, etc.The training samples are used to “train” the classifier.

2. Determine the input “feature” representation of the learned function.The accuracy of the learned function depends on how the input object isrepresented. Typically, the input object is transformed into a featurevector, which contains a number of features that are descriptive of theobject. The number of features should not be too large, because of thecurse of dimensionality; but should be large enough to accuratelypredict the output. The features might include a set of bacterialspecies or serotypes present in a food or environmental sample derivedas described herein.

3. Determine the structure of the learned function and correspondinglearning algorithm. A learning algorithm is chosen, e.g., artificialneural networks, decision trees, Bayes classifiers or support vectormachines. The learning algorithm is used to build the classifier.

4. Build the classifier (e.g. classification model). The learningalgorithm is run on the gathered training set. Parameters of thelearning algorithm may be adjusted by optimizing performance on a subset(called a validation set) of the training set, or via cross-validation.After parameter adjustment and learning, the performance of thealgorithm may be measured on a test set of naive samples that isseparate from the training set.

Once the classifier (e.g. classification model) is determined asdescribed above, it can be used to classify a sample, e.g., that of foodsample or environment that is being analyzed by the methods of theinvention.

Unsupervised learning approaches can also be used with the invention.Clustering is an unsupervised learning approach wherein a clusteringalgorithm correlates a series of samples without the use the labels. Themost similar samples are sorted into “clusters.” A new sample could besorted into a cluster and thereby classified with other members that itmost closely associates.

Digital Processing Device

In some aspects, the disclosed provides quality control methods ormethods to assess a risk associated with a food, with a hospital, with aclinic, or any other location where the presence of a bacterium poses acertain risk to one or more subjects. In many instances, systems,platforms, software, networks, and methods described herein include adigital processing device, or use of the same. In further embodiments,the digital processing device includes one or more hardware centralprocessing units (CPUs), i.e., processors that carry out the device'sfunctions, such as the automated sequencing apparatus disclosed hereinor a computer system used in the analyses of a plurality of nucleic acidsequencing reads from samples derived from a food processing facility orfrom any other facility, such as a hospital a clinical or another. Instill further embodiments, the digital processing device furthercomprises an operating system configured to perform executableinstructions. In some embodiments, the digital processing device isoptionally connected a computer network. In further embodiments, thedigital processing device is optionally connected to the Internet suchthat it accesses the World Wide Web. In still further embodiments, thedigital processing device is optionally connected to a cloud computinginfrastructure. In other embodiments, the digital processing device isoptionally connected to an intranet. In other embodiments, the digitalprocessing device is optionally connected to a data storage device. Inother embodiments, the digital processing device could be deployed onpremise or remotely deployed in the cloud.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers,handheld computers, Internet appliances, mobile smartphones, tabletcomputers, personal digital assistants, video game consoles, andvehicles. Those of skill in the art will recognize that many smartphonesare suitable for use in the system described herein. Those of skill inthe art will also recognize that select televisions, video players, anddigital music players with optional computer network connectivity aresuitable for use in the system described herein. Suitable tabletcomputers include those with booklet, slate, and convertibleconfigurations, known to those of skill in the art. In many aspects, thedisclosure contemplates any suitable digital processing device that caneither be deployed to a food processing facility, or is used within saidfood processing facility to process and analyze a variety of nucleicacids from a variety of samples.

In some embodiments, a digital processing device includes an operatingsystem configured to perform executable instructions. The operatingsystem is, for example, software, including programs and data, whichmanages the device's hardware and provides services for execution ofapplications. Those of skill in the art will recognize that suitableserver operating systems include, by way of non-limiting examples,FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle®Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in theart will recognize that suitable personal computer operating systemsinclude, by way of non-limiting examples, Microsoft® Windows®, Apple®Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. Insome embodiments, the operating system is provided by cloud computing.Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia®Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google®Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS,Linux®, and Palm® WebOS®.

In some embodiments, a digital processing device includes a storageand/or memory device. The storage and/or memory device is one or morephysical apparatuses used to store data or programs on a temporary orpermanent basis. In some embodiments, the device is volatile memory andrequires power to maintain stored information. In some embodiments, thedevice is non-volatile memory and retains stored information when thedigital processing device is not powered. In further embodiments, thenon-volatile memory comprises flash memory. In some embodiments, thenon-volatile memory comprises dynamic random-access memory (DRAM). Insome embodiments, the non-volatile memory comprises ferroelectric randomaccess memory (FRAM). In some embodiments, the non-volatile memorycomprises phase-change random access memory (PRAM). In otherembodiments, the device is a storage device including, by way ofnon-limiting examples, CD-ROMs, DVDs, flash memory devices, magneticdisk drives, magnetic tapes drives, optical disk drives, and cloudcomputing based storage. In further embodiments, the storage and/ormemory device is a combination of devices such as those disclosedherein.

In some embodiments, a digital processing device includes a display tosend visual information to a user. In some embodiments, the display is acathode ray tube (CRT). In some embodiments, the display is a liquidcrystal display (LCD). In further embodiments, the display is a thinfilm transistor liquid crystal display (TFT-LCD). In some embodiments,the display is an organic light emitting diode (OLED) display. Invarious further embodiments, on OLED display is a passive-matrix OLED(PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments,the display is a plasma display. In other embodiments, the display is avideo projector. In still further embodiments, the display is acombination of devices such as those disclosed herein.

In some embodiments, a digital processing device includes an inputdevice to receive information from a user. In some embodiments, theinput device is a keyboard. In some embodiments, the input device is apointing device including, by way of non-limiting examples, a mouse,trackball, track pad, joystick, game controller, or stylus. In someembodiments, the input device is a touch screen or a multi-touch screen.In other embodiments, the input device is a microphone to capture voiceor other sound input. In other embodiments, the input device is a videocamera to capture motion or visual input. In still further embodiments,the input device is a combination of devices such as those disclosedherein.

In some embodiments, a digital processing device includes a digitalcamera. In some embodiments, a digital camera captures digital images.In some embodiments, the digital camera is an autofocus camera. In someembodiments, a digital camera is a charge-coupled device (CCD) camera.In further embodiments, a digital camera is a CCD video camera. In otherembodiments, a digital camera is a complementarymetal-oxide-semiconductor (CMOS) camera. In some embodiments, a digitalcamera captures still images. In other embodiments, a digital cameracaptures video images. In various embodiments, suitable digital camerasinclude 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and higher megapixelcameras, including increments therein. In some embodiments, a digitalcamera is a standard definition camera. In other embodiments, a digitalcamera is an HD video camera. In further embodiments, an HD video cameracaptures images with at least about 1280×about 720 pixels or at leastabout 1920×about 1080 pixels. In some embodiments, a digital cameracaptures color digital images. In other embodiments, a digital cameracaptures grayscale digital images. In various embodiments, digitalimages are stored in any suitable digital image format. Suitable digitalimage formats include, by way of non-limiting examples, JointPhotographic Experts Group (JPEG), JPEG 2000, Exchangeable image fileformat (Exif), Tagged Image File Format (TIFF), RAW, Portable NetworkGraphics (PNG), Graphics Interchange Format (GIF), Windows® bitmap(BMP), portable pixmap (PPM), portable graymap (PGM), portable bitmapfile format (PBM), and WebP. In various embodiments, digital images arestored in any suitable digital video format. Suitable digital videoformats include, by way of non-limiting examples, AVI, MPEG, Apple®QuickTime®, MP4, AVCHD®, Windows Media®, DivX™, Flash Video, Ogg Theora,WebM, and RealMedia.

Non-Transitory Computer Readable Storage Medium

In many aspects, the systems, platforms, software, networks, and methodsdisclosed herein include one or more non-transitory computer readablestorage media encoded with a program including instructions executableby the operating system of an optionally networked digital processingdevice. For instance, in some aspects, the methods comprise creatingdata files associated with a plurality of sequencing reads from aplurality of samples associated with a food processing facility. Infurther embodiments, a computer readable storage medium is a tangiblecomponent of a digital processing device. In still further embodiments,a computer readable storage medium is optionally removable from adigital processing device. In some embodiments, a computer readablestorage medium includes, by way of non-limiting examples, CD-ROMs, DVDs,flash memory devices, solid state memory, magnetic disk drives, magnetictape drives, optical disk drives, cloud computing systems and services,and the like. In some cases, the program and instructions arepermanently, substantially permanently, semi-permanently, ornon-transitorily encoded on the media.

Computer Program

In some embodiments, the systems, platforms, software, networks, andmethods disclosed herein include at least one computer program. Acomputer program includes a sequence of instructions, executable in thedigital processing device's CPU, written to perform a specified task. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a computer program may be written in various versions ofvarious languages. In some embodiments, a computer program comprises onesequence of instructions. In some embodiments, a computer programcomprises a plurality of sequences of instructions. In some embodiments,a computer program is provided from one location. In other embodiments,a computer program is provided from a plurality of locations. In variousembodiments, a computer program includes one or more software modules.In various embodiments, a computer program includes, in part or inwhole, one or more web applications, one or more mobile applications,one or more standalone applications, one or more web browser plug-ins,extensions, add-ins, or add-ons, or combinations thereof.

Web Application

In some embodiments, a computer program includes a web application. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a web application, in various embodiments, utilizes oneor more software frameworks and one or more database systems. In someembodiments, a web application is created upon a software framework suchas Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a webapplication utilizes one or more database systems including, by way ofnon-limiting examples, relational, non-relational, object oriented,associative, and XML database systems. In further embodiments, suitablerelational database systems include, by way of non-limiting examples,Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the artwill also recognize that a web application, in various embodiments, iswritten in one or more versions of one or more languages. A webapplication may be written in one or more markup languages, presentationdefinition languages, client-side scripting languages, server-sidecoding languages, database query languages, or combinations thereof. Insome embodiments, a web application is written to some extent in amarkup language such as Hypertext Markup Language (HTML), ExtensibleHypertext Markup Language (XHTML), or eXtensible Markup Language (XML).In some embodiments, a web application is written to some extent in apresentation definition language such as Cascading Style Sheets (CSS).In some embodiments, a web application is written to some extent in aclient-side scripting language such as Asynchronous Javascript and XML(AJAX), Flash® Actionscript, Javascript, or Silverlight®. In someembodiments, a web application is written to some extent in aserver-side coding language such as Active Server Pages (ASP),ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor(PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In someembodiments, a web application is written to some extent in a databasequery language such as Structured Query Language (SQL). In someembodiments, a web application integrates enterprise server productssuch as IBM® Lotus Domino®. A web application for providing a careerdevelopment network for artists that allows artists to uploadinformation and media files, in some embodiments, includes a mediaplayer element. In various further embodiments, a media player elementutilizes one or more of many suitable multimedia technologies including,by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple®QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile applicationprovided to a mobile digital processing device. In some embodiments, themobile application is provided to a mobile digital processing device atthe time it is manufactured. In other embodiments, the mobileapplication is provided to a mobile digital processing device via thecomputer network described herein.

In view of the disclosure provided herein, a mobile application iscreated by techniques known to those of skill in the art using hardware,languages, and development environments known to the art. Those of skillin the art will recognize that mobile applications are written inseveral languages. Suitable programming languages include, by way ofnon-limiting examples, C, C++, C#, Objective-C, Java™, Javascript,Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML withor without CSS, or combinations thereof.

Suitable mobile application development environments are available fromseveral sources. Commercially available development environmentsinclude, by way of non-limiting examples, AirplaySDK, alcheMo,Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework,Rhomobile, and WorkLight Mobile Platform. Other development environmentsare available without cost including, by way of non-limiting examples,Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile devicemanufacturers distribute software developer kits including, by way ofnon-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK,BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, andWindows® Mobile SDK.

Those of skill in the art will recognize that several commercial forumsare available for distribution of mobile applications including, by wayof non-limiting examples, Apple® App Store, Android™ Market, BlackBerry®App World, App Store for Palm devices, App Catalog for webOS, Windows®Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, andNintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Objective-C,COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET,or combinations thereof. Compilation is often performed, at least inpart, to create an executable program. In some embodiments, a computerprogram includes one or more executable complied applications.

Software Modules

The systems, platforms, software, networks, and methods disclosed hereininclude, in various embodiments, software, server, and database modules.In view of the disclosure provided herein, software modules are createdby techniques known to those of skill in the art using machines,software, and languages known to the art. The software modules disclosedherein are implemented in a multitude of ways. In various embodiments, asoftware module comprises a file, a section of code, a programmingobject, a programming structure, or combinations thereof. In furthervarious embodiments, a software module comprises a plurality of files, aplurality of sections of code, a plurality of programming objects, aplurality of programming structures, or combinations thereof. In variousembodiments, the one or more software modules comprise, by way ofnon-limiting examples, a web application, a mobile application, and astandalone application. In some embodiments, software modules are in onecomputer program or application. In other embodiments, software modulesare in more than one computer program or application. In someembodiments, software modules are hosted on one machine. In otherembodiments, software modules are hosted on more than one machine. Infurther embodiments, software modules are hosted on cloud computingplatforms. In some embodiments, software modules are hosted on one ormore machines in one location. In other embodiments, software modulesare hosted on one or more machines in more than one location.

Embodiments

EMBODIMENT 1. An embodiment comprising: (a) deploying an assay to one ormore food processing facilities; (b) performing a sequencing reaction ofa food sample or of an environmental sample from said one or more foodprocessing facilities; (c) transmitting an electronic communicationcomprising a data set associated with said sequencing reaction of saidfood sample or of said environmental sample from said one or more foodprocessing facilities to a server; and (d) scanning, by a computer, atleast a fraction of said transmitted data set for one or more genesassociated with a microorganism.

EMBODIMENT 2. The method of embodiment 1, wherein said scanning scansfewer than 0.001%, 0.01%, 0.1%, 1% of said transmitted data set for oneor more genes associated with said microorganism.

EMBODIMENT 3. The method of embodiment 1, wherein said sequencingreaction is a pore sequencing reaction, a next generation sequencingreaction, a shotgun next generation sequencing, or Sanger sequencing.

EMBODIMENT 4. The method of embodiment 3, wherein said sequencingreaction is a pore sequencing reaction.

EMBODIMENT 5. The method of embodiment 4, wherein said pore sequencingreaction distinguishes an epigenetic pattern on a nucleic acid from saidfood sample or from said environmental sample.

EMBODIMENT 6. The method of embodiment 5, wherein said epigeneticpattern is a methylation pattern.

EMBODIMENT 7. The method of embodiment 1, wherein said microorganism ispre-selected by a customer.

EMBODIMENT 8. The method of embodiment 1, further comprising scanning,by a computer, at least a fraction of said transmitted data set for oneor more genes associated with two or more microorganisms.

EMBODIMENT 9. The method of embodiment 1, wherein said microorganism isselected from the group consisting of: a microorganism of the Salmonellagenus, a microorganism of the Campylobacter genus, a microorganism ofthe Listeria genus, and a microorganism of the Escherichia genus.

EMBODIMENT 10. The method of embodiment 1, wherein said food sample is aperishable.

EMBODIMENT 11. The method of embodiment 10, wherein said perishable is ameat.

EMBODIMENT 12. The method of embodiment 11, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 13. The method of embodiment 8, wherein said perishable is afruit, an egg, a vegetable, a produce or a legume.

EMBODIMENT 14. The method of embodiment 1, wherein said environmentalsample is a surface swab or a surface rinse of said one or more foodprocessing facilities.

EMBODIMENT 15. The method of embodiment 1, wherein said environmentalsample is a food storage container, a food handling equipment from saidone or more food processing facilities, or a piece of clothing from aworker of said one or more food processing facilities.

EMBODIMENT 16. The method of embodiment 1, further comprising amplifyingor enriching one or more nucleic acids of said food sample or of saidenvironmental sample prior to performing said sequencing reaction.

EMBODIMENT 17. The method of embodiment 1, further comprising adding atleast one barcode to one or more nucleic acids of said food sample or ofsaid environmental sample prior to performing said sequencing reaction.

EMBODIMENT 18. The method of embodiment 17, further comprising creating,in a computer, a data file that associates said at least one barcodewith a source of said food sample or of said environmental sample.

EMBODIMENT 19. The method of embodiment 17, further comprising adding aplurality of mutually exclusive barcodes to a plurality of foodprocessing facilities.

EMBODIMENT 20. The method of embodiment 1, wherein said scanningcomprises scanning said transmitted data set for one or more polymorphicgene regions.

EMBODIMENT 21. The method of embodiment 20, wherein said one or morepolymorphic regions comprise one or more single nucleotide polymorphisms(SNP's), one or more restriction fragment length polymorphisms (RFLP's),one or more short tandem repeats (STRs), one or more variable number oftandem repeats (VNTR's), one or more hypervariable regions, one or moreminisatellites, one or more dinucleotide repeats, one or moretrinucleotide repeats, one or more tetranucleotide repeats, one or moresimple sequence repeats, one or more indel, or one or more insertionelements.

EMBODIMENT 22. The method of embodiment 20, wherein said one or morepolymorphic regions comprise one or more single nucleotide polymorphisms(SNP's).

EMBODIMENT 23. The method of embodiment 1, wherein said sequencingreaction differentiates a live microorganism from a dead microorganism.

EMBODIMENT 24. The method of embodiment 1, wherein said sequencingreaction differentiates a resident microorganism as compared to atransient microorganism.

EMBODIMENT 25. The method of embodiment 1, wherein said methoddistinguishes a microorganism from an Escherichia genus from amicroorganism of a Citrobacter genus or a Shiga-Toxin producing E. coli(STEC) from a non-STEC E. coli.

EMBODIMENT 26. An embodiment comprising: (a) deploying an assay to oneor more food processing facilities; (b) performing a sequencing reactionof a food sample or of an environmental sample from said one or morefood processing facilities; (c) transmitting an electronic communicationcomprising a data set associated with said sequencing reaction of saidfood sample or of said environmental sample from said one or more foodprocessing facilities to a server; and (d) scanning, by a computer, atleast a fraction of said transmitted data set for one or more genesassociated with a microorganism. Copy language of issued claim

EMBODIMENT 27. An embodiment comprising: (a) deploying an assay to oneor more food processing facilities; (b) performing a sequencing reactionof a food sample or of an environmental sample from said one or morefood processing facilities, wherein said sample comprises a targetnucleic acid comprising a periodic or a non-periodic barcode; (c)transmitting an electronic communication comprising a data setassociated with said sequencing reaction of said food sample or of saidenvironmental sample from said one or more food processing facilities toa server; and (d) scanning, by a computer, at least a fraction of saidtransmitted data set for one or more genes associated with amicroorganism, wherein said fraction is in comparison to a data set of asubstantially complete sequencing reaction, wherein said fraction ofsaid transmitted data set comprises a number of sequencing reads or anumber of sequenced nucleotide bases.

EMBODIMENT 28. An embodiment comprising: (a) obtaining a plurality ofnucleic acid sequences from a sample; (b) scanning, by a computer, atleast a fraction of said plurality of said nucleic acid sequences for aplurality of nucleic acid regions from one or more microorganismsselected from the group consisting of: a microorganism of the Salmonellagenus, a microorganism of the Campylobacter genus, a microorganism ofthe Listeria genus, and a microorganism of the Escherichia genus,wherein said scanning characterizes said one or more microorganisms withgreater than 99.5% sensitivity.

EMBODIMENT 29. The method of embodiment 28, wherein said sample is afood sample or an environmental sample associated with said food sample.

EMBODIMENT 30. The method of embodiment 28, wherein said sample is anon-food sample.

EMBODIMENT 31. The method of embodiment 28, wherein said samplecomprises blood, plasma, urine, tissue, faces, bone marrow, saliva orcerebrospinal fluid.

EMBODIMENT 32. The method of embodiment 28, wherein said scanningcharacterizes said one or more microorganisms with greater than 99%sensitivity.

EMBODIMENT 33. The method of embodiment 32, wherein said scanningcharacterizes said one or more microorganisms with greater than 99.9%,99.99%, or 99.999% sensitivity.

EMBODIMENT 34. The method of embodiment 33, wherein said scanningcharacterizes said one or more microorganisms with greater than 99.5%specificity.

EMBODIMENT 35. The method of embodiment 34, wherein said scanningcharacterizes said one or more microorganisms with greater than 99%specificity.

EMBODIMENT 36. The method of embodiment 35, wherein said scanningcharacterizes said one or more microorganisms with greater than 99.9%,99.99%, or 99.999% specificity.

EMBODIMENT 37. The method of embodiment 28, wherein said scanningcharacterizes said one or more microorganisms with greater than 99.5%sensitivity and greater than 99% specificity.

EMBODIMENT 38. The method of embodiment 28, wherein a scanning of nomore than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid regions within saidplurality of nucleic acid sequences characterizes said one or moremicroorganisms with greater than 99.5% sensitivity.

EMBODIMENT 39. The method of embodiment 38, wherein a scanning of nomore than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid regions within saidplurality of nucleic acid sequences characterizes said one or moremicroorganisms with greater than 99.9% sensitivity.

EMBODIMENT 40. The method of embodiment 28, wherein a scanning of nomore than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid regions within saidplurality of nucleic acid sequences characterizes said one or moremicroorganisms with greater than 99.5% specificity.

EMBODIMENT 41. The method of embodiment 40, wherein a scanning of nomore than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid regions within saidplurality of nucleic acid sequences characterizes said one or moremicroorganisms with greater than 99.9% specificity.

EMBODIMENT 42. The method of embodiment 41, wherein said method hasfewer than 0.1% of a false positive identification rate.

EMBODIMENT 43. The method of embodiment 28, wherein said plurality ofnucleic acid sequences comprise complementary DNA (cDNA) sequences.

EMBODIMENT 44. The method of embodiment 28, wherein said plurality ofnucleic acid sequences comprise ribonucleic acid (RNA) sequences.

EMBODIMENT 45. The method of embodiment 28, wherein said plurality ofnucleic acid sequences comprise genomic deoxyribonucleic acid (gDNA)sequences.

EMBODIMENT 46. The method of embodiment 28, wherein said plurality ofnucleic acid sequences comprise a mixture of cDNA, RNA, and gDNAsequences.

EMBODIMENT 47. The method of embodiment 28, wherein said scanningcomprises scanning said plurality of said nucleic acid sequences for oneor more polymorphic gene regions associated with said microorganisms.

EMBODIMENT 48. The method of embodiment 47, wherein said one or morepolymorphic regions comprise a gene coding region associated with saidmicroorganisms.

EMBODIMENT 49. The method of embodiment 47, wherein said one or morepolymorphic regions comprise a regulatory region associated with saidmicroorganisms.

EMBODIMENT 50. The method of embodiment 47, wherein said one or morepolymorphic regions is selected from the group consisting of one or moresingle nucleotide polymorphisms (SNPs), one or more restriction fragmentlength polymorphisms (RFLPs), one or more short tandem repeats (STRs),one or more variable number of tandem repeats (VNTRs), one or morehypervariable regions, one or more minisatellites, one or moredinucleotide repeats, one or more trinucleotide repeats, one or moretetranucleotide repeats, one or more simple sequence repeats, one ormore insertion elements, or one or more epigenetic modifications.

EMBODIMENT 51. The method of embodiment 28, wherein said obtaining ofsaid plurality of nucleic acid sequences comprises sequencing orhybridizing said plurality of nucleic acid sequences.

EMBODIMENT 52. The method of embodiment 51, wherein said sequencingreaction is a pore sequencing reaction, a next generation sequencingreaction, a shotgun next generation sequencing, or Sanger sequencing.

EMBODIMENT 53. The method of embodiment 52, wherein said sequencingreaction is a pore sequencing reaction.

EMBODIMENT 54. The method of embodiment 53, wherein said pore sequencingreaction distinguishes an epigenetic pattern on a nucleic acid from saidfood sample or from said environmental sample.

EMBODIMENT 55. The method of embodiment 54, wherein said epigeneticpattern is a methylation pattern.

EMBODIMENT 56. The method of embodiment 28, wherein said microorganismof the Salmonella genus has a serotype selected from the groupconsisting of: Enteritidis, Typhimurium, Newport, Javiana, Infantis,Montevideo, Heidelberg, Muenchen, Saintpaul, Oranienburg, Braenderup,Paratyphi B var. L(+) Tartrate+, Agona, Thompson, and Kentucky.

EMBODIMENT 57. The method of embodiment 56, wherein said microorganismof the Salmonella genus is of the serotype Enteritidis.

EMBODIMENT 58. The method of embodiment 56, wherein said microorganismof the Salmonella genus is of the serotype Typhimurium.

EMBODIMENT 59. The method of embodiment 56, wherein said microorganismof the Salmonella genus is of the serotype Newport.

EMBODIMENT 60. The method of embodiment 56, wherein said microorganismof the Salmonella genus is of the serotype Javiana.

EMBODIMENT 61. The method of embodiment 56, wherein said microorganismof the Escherichia genus has a serotype selected from the groupconsisting of: O103, O111, O121, O145, O26, O45, and O157.

EMBODIMENT 62. The method of embodiment 61, wherein said microorganismof the Escherichia genus is E. coli O157:H7.

EMBODIMENT 63. The method of embodiment 28, wherein said scanningdistinguishes said microorganism of the Escherichia genus from amicroorganism of the Citrobacter genus.

EMBODIMENT 64. The method of embodiment 28, wherein said microorganismof the Listeria genus has a serotype selected from the group consistingof: 2a, 1/2b, 1/2c, 3a, 3b, 3c, 4a, 4b, 4ab, 4c, 4d, and 4e.

EMBODIMENT 65. The method of embodiment 28, wherein said microorganismof the Campylobacter genus is C. jejuni, C. lari, and C. coli.

EMBODIMENT 66. An embodiment comprising: (a) sequencing a plurality ofnucleic acid sequences from a food sample or from an environmentalsample associated with said food sample for a period of time; and (b)performing an assay on said food sample or said environment associatedwith said food sample if said sequencing for said period of timeidentifies a threshold level of nucleic acid sequences from amicroorganism in said food sample.

EMBODIMENT 67. The method of embodiment 66, wherein said period of timeis less than 30 minutes.

EMBODIMENT 68. The method of embodiment 66, wherein said period of timeis less than 20 minutes.

EMBODIMENT 69. The method of embodiment 66, wherein said threshold is nomore than 0.001%, 0.01%, 0.1%, or 1%, of nucleic acid sequences fromsaid microorganism.

EMBODIMENT 70. The method of embodiment 66, further comprisingperforming an amplification reaction on said plurality of nucleic acidsequences prior to sequencing.

EMBODIMENT 71. The method of embodiment 66, wherein said sequencing is apore sequencing reaction.

EMBODIMENT 72. The method of embodiment 66, wherein said assay is aserotyping assay, a culturing assay, a Pulse Field Gel Electrophoresis(PFGE) assay, a RiboPrinter® assay, a q-PCR assay, a Sanger sequencingassay, an ELISA assay, a Whole Genome Sequencing (WGS) assay, a targetedsequencing assay, or a shotgun metagenomics assay.

EMBODIMENT 73. The method of embodiment 66, wherein said microorganismis selected from the group consisting of: a microorganism of theSalmonella genus, a microorganism of the Campylobacter genus, amicroorganism of the Listeria genus, and a microorganism of theEscherichia genus.

EMBODIMENT 74. The method of embodiment 73, wherein said microorganismof the Salmonella genus has a serotype selected from the groupconsisting of: Enteritidis, Typhimurium, Newport, Javiana, Infantis,Montevideo, Heidelberg, Muenchen, Saintpaul, Oranienburg, Braenderup,Paratyphi B var. L(+) Tartrate+, Agona, Thompson, and Kentucky.

EMBODIMENT 75. The method of embodiment 73, wherein said microorganismof the Escherichia genus has a serotype selected from the groupconsisting of: O103, O111, O121, O145, O26, O45, and O157.

EMBODIMENT 76. The method of embodiment 73, wherein said microorganismof the Escherichia genus is E. coli O157:H7.

EMBODIMENT 77. The method of embodiment 73, wherein said microorganismof the Listeria genus has a serotype selected from the group consistingof: 2a, 1/2b, 1/2c, 3a, 3b, 3c, 4a, 4b, 4ab, 4c, 4d, and 4e.

EMBODIMENT 78. The method of embodiment 73, wherein said microorganismof the Campylobacter genus is C. jejunis, C. lari, or C. coli.

EMBODIMENT 79. The method of embodiment 66, wherein said plurality ofnucleic acid sequences comprise complementary DNA (cDNA) sequences.

EMBODIMENT 80. The method of embodiment 66, wherein said plurality ofnucleic acid sequences comprise ribonucleic acid (RNA) sequences.

EMBODIMENT 81. The method of embodiment 66, wherein said plurality ofnucleic acid sequences comprise genomic deoxyribonucleic acid (gDNA)sequences.

EMBODIMENT 82. The method of embodiment 66, wherein said plurality ofnucleic acid sequences comprise a mixture of cDNA, RNA, and gDNAsequences.

EMBODIMENT 83. The method of embodiment 66, wherein said food sample isa perishable.

EMBODIMENT 84. The method of embodiment 83, wherein said perishable is ameat.

EMBODIMENT 85. The method of embodiment 84, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 86. The method of embodiment 83, wherein said perishable is afruit, an egg, a vegetable, a produce or a legume.

EMBODIMENT 87. The method of embodiment 66, wherein said environmentalsample is a surface swab or a surface rinse of said environment.

EMBODIMENT 88. The method of embodiment 66, wherein said environmentalsample is a food storage container, a food handling equipment, or apiece of clothing from a worker of said environment associated with saidfood sample.

EMBODIMENT 89. An embodiment comprising: (a) obtaining a first pluralityof nucleic acid sequences from a first sample of a food processingfacility; (b) creating a data file in a computer that associates one ormore of said first plurality of nucleic acid sequences with said foodprocessing facility; (c) obtaining a second plurality of nucleic acidsequences from a second sample of said food processing facility; and (d)scanning a plurality of sequences from said second plurality of nucleicacid sequences for one or more sequences associated with said foodprocessing facility in (b).

EMBODIMENT 90. The method of embodiment 89, wherein said data fileassociates a strain of said microorganism with said food processingfacility.

EMBODIMENT 91. The method of embodiment 89, wherein said first sample,said second sample, or both comprises a plurality of sequences from aplurality of microorganisms.

EMBODIMENT 92. The method of embodiment 89, wherein at least one of saidplurality of microorganisms is a non-pathogenic microorganism.

EMBODIMENT 93. The method of embodiment 89, wherein at least one of saidplurality of microorganisms is a pathogenic microorganism.

EMBODIMENT 94. The method of embodiment 93, wherein said pathogenicmicroorganism is selected from the group consisting of a gram-negativebacteria, a gram-positive bacteria, a protozoa, a viruses, and a fungi.

EMBODIMENT 95. The method of embodiment 94, wherein said gram-negativebacteria is a Salmonella bacterium.

EMBODIMENT 96. The method of embodiment 94, wherein said gram-negativebacteria is an Escherichia bacterium.

EMBODIMENT 97. The method of embodiment 94, wherein said gram-positivebacteria is a Listeria bacterium.

EMBODIMENT 98. The method of embodiment 94, wherein said gram-negativebacteria is a Campylobacter bacterium.

EMBODIMENT 99. The method of embodiment 89, further comprising obtaininga third plurality of nucleic acid sequences from an additional sample ofsaid food processing facility.

EMBODIMENT 100. The method of embodiment 89, wherein said first sample,said second sample, or both is a perishable.

EMBODIMENT 101. The method of embodiment 100, wherein said perishable isa meat.

EMBODIMENT 102. The method of embodiment 100, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 103. The method of embodiment 101, wherein said perishableitem is a fruit, an egg, a vegetable, a produce or a legume.

EMBODIMENT 104. The method of embodiment 89, wherein said first sample,said second sample, or both is a surface swab or a surface rinse of saidenvironment.

EMBODIMENT 105. The method of embodiment 89, wherein said first sample,said second sample, or both is a food storage container, a food handlingequipment, or a piece of clothing from a worker of said environmentassociated with said food sample.

EMBODIMENT 106. The method of embodiment 89, wherein at least onebarcode is added to said first plurality of nucleic acid sequences, saidsecond plurality of nucleic acid sequences or both.

EMBODIMENT 107. The method of embodiment 106, wherein said at least onebarcode is associated with said data file of (b), thereby associatingsaid at least one barcode with said food processing facility.

EMBODIMENT 108. The method of embodiment 89, wherein obtaining saidfirst plurality of nucleic acid sequences, said second plurality ofnucleic acid sequences, or both comprises performing a sequencingreaction or a hybridization assay.

EMBODIMENT 109. The method of embodiment 105, wherein said sequencingreaction is a pore sequencing reaction, a next generation sequencingreaction, a shotgun next generation sequencing, or Sanger sequencing.

EMBODIMENT 110. The method of embodiment 109, wherein said sequencingreaction is a pore sequencing reaction.

EMBODIMENT 111. The method of embodiment 110, wherein said poresequencing reaction distinguishes an epigenetic pattern on a nucleicacid from said food sample or from said environmental sample.

EMBODIMENT 112. The method of embodiment 110, wherein said epigeneticpattern is a methylation pattern.

EMBODIMENT 113. An embodiment comprising: (a) obtaining a first sampleof a food processing facility; (b) sequencing said first sample of saidfood processing facility, thereby generating a first set of sequencingdata from said food processing facility; (c) obtaining a second sampleof said food processing facility; (d) sequencing said second sample ofsaid food processing facility, thereby generating a second set ofsequencing data from said food processing facility; and (e) comparingsaid second set of sequencing data to said first set of sequencing data;and (d) decontaminating said food processing facility if said comparingidentifies a pathogenic microorganism in said food processing facility.

EMBODIMENT 114. An embodiment comprising (a) obtaining a first pluralityof nucleic acid sequences from a first sample of a food processingfacility; (b) obtaining a second plurality of nucleic acid sequencesfrom a second food sample of said food processing facility; and (c)performing sequence alignments in a computer between said firstplurality of nucleic acid sequences and said second plurality of nucleicacid sequences thereby determining a similarity between said firstsample and said second sample from said food processing facility.

EMBODIMENT 115. An embodiment comprising: (a) adding a reagent to aplurality of nucleic acid molecules from a food sample or from anenvironmental sample associated with said food sample, thereby forming amodified plurality of nucleic acid molecules, whereby said reagent (i)modifies a structure of or interacts with a plurality of nucleic acidmolecules derived from one or more dead microorganisms; and (ii) doesnot modify a structure of a nucleic acid molecule derived from one ormore live microorganisms; thereby providing a modified plurality ofnucleic acid molecules; and (b) sequencing by a sequencing reaction saidmodified plurality of nucleic acid molecules, thereby distinguishing oneor more live organisms from said food sample or from said environmentalsample associated with said food sample.

EMBODIMENT 116. The method of embodiment 113, wherein said sequencingreaction comprises pore sequencing.

EMBODIMENT 117. The method of embodiment 113, wherein said food sampleis stressed, shocked or processed prior to adding said reagent to saidplurality of nucleic acid molecules.

EMBODIMENT 118. The method of embodiment 113, further comprisingincubating said food sample in a growth medium prior to performing saidsequencing reaction.

EMBODIMENT 119. The method of embodiment 113, wherein said reagent is aphotoreactive DNA-binding dye.

EMBODIMENT 120. The method of embodiment 119, wherein said photoreactiveDNA-binding dye is propidium monoazide or a derivative thereof.

EMBODIMENT 121. The method of embodiment 113, wherein said reagent is aDNA intercalating reagent.

EMBODIMENT 122. The method of embodiment 113, further comprisingperforming an amplification reaction prior to sequencing said modifiedplurality of nucleic acid molecules.

EMBODIMENT 123. The method of embodiment 113, wherein said food sampleis a perishable.

EMBODIMENT 124. The method of embodiment 123, wherein said perishable isa meat.

EMBODIMENT 125. The method of embodiment 124, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 126. The method of embodiment 123, wherein said perishable isa fruit, an egg, a vegetable, or a legume.

EMBODIMENT 127. The method of embodiment 115, wherein said environmentalsample is a surface swab or a surface rinse of said environment.

EMBODIMENT 128. The method of embodiment 115, wherein said environmentalsample is a food storage container, a food handling equipment, or apiece of clothing from a worker of said environment associated with saidfood sample.

EMBODIMENT 129. An embodiment comprising performing a pore sequencingreaction on a plurality of nucleic acid molecules from a food sample orfrom an environmental sample associated with said food sample, wherebysaid pore sequencing reaction distinguishes one or more nucleic acidmolecules derived from a dead microorganism from one or more nucleicacid molecules derived from a live microorganism based on a methylationpattern or another epigenetic pattern of said one or more nucleic acidmolecules derived from said dead microorganism.

EMBODIMENT 130. The method of embodiment 129, wherein said poresequencing reaction is a nanopore sequencing reaction.

EMBODIMENT 131. A method comprising: (a) obtaining a plurality ofnucleic acid sequences of a food sample or of an environmental samplefrom a food processing facility; (b) performing a first assay in saidplurality of nucleic acid sequences of said food sample, whereby saidassay predicts a presence or predicts an absence of a microorganism insaid food sample; and (c) determining, based on said predicted presenceor said predicted absence of said microorganism of (b) whether toperform a second assay, whereby a sensitivity of said second assay isselected to determine a genus, a species, a serotype, a sub-serotype, ora strain of said microorganism.

EMBODIMENT 132. The method of embodiment 131, wherein said first assayand said second assay are identical.

EMBODIMENT 133. The method of embodiment 131, wherein said first assayand said second assay have distinct sensitivities.

EMBODIMENT 134. The method of embodiment 131, wherein said first assay,said second assay or both comprise a sequencing assay.

EMBODIMENT 135. The method of embodiment 134, wherein said sequencingassay comprises a pore sequencing reaction, a next generation sequencingreaction, a shotgun next generation sequencing, or Sanger sequencing.

EMBODIMENT 136. The method of embodiment 134, wherein said sequencingreaction is a pore sequencing reaction.

EMBODIMENT 137. The method of embodiment 134, wherein said poresequencing reaction distinguishes an epigenetic pattern on a nucleicacid from said food sample or from said environmental sample.

EMBODIMENT 138. The method of embodiment 137, wherein said epigeneticpattern is a methylation pattern.

EMBODIMENT 139. The method of embodiment 131, wherein said first assay,said second assay, or both comprise a polymerase chain reaction (PCR)assay.

EMBODIMENT 140. The method of embodiment 131, wherein said first assay,said second assay, or both comprise an enzyme-linked immunosorbent(ELISA) assay.

EMBODIMENT 141. The method of embodiment 131, wherein said first assay,said second assay, or both comprise an enzyme-linked fluorescent assay(ELFA) assay.

EMBODIMENT 142. The method of embodiment 131, wherein said first assay,said second assay, or both comprise a serotyping assay.

EMBODIMENT 143. The method of embodiment 131, wherein said microorganismis selected from the group consisting of: a microorganism of theSalmonella genus, a microorganism of the Campylobacter genus, amicroorganism of the Listeria genus, a microorganism of the Escherichiagenus, a virus, a parasite, and a fungi.

EMBODIMENT 144. The method of embodiment 131, wherein said food sampleis a perishable.

EMBODIMENT 145. The method of embodiment 144, wherein said perishable isa meat.

EMBODIMENT 146. The method of embodiment 145, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 147. The method of embodiment 144, wherein said perishable isa fruit, an egg, a vegetable, a produce or a legume.

EMBODIMENT 148. The method of embodiment 144, wherein said environmentalsample is a surface swab or a surface rinse of said environment.

EMBODIMENT 149. The method of embodiment 144, wherein said environmentalsample is a food storage container, a food handling equipment, or apiece of clothing from a worker of said environment associated with saidfood processing facility.

EMBODIMENT 150. The method of embodiment 131, wherein said performing ofsaid first assay and said performing of said second assay predicts saidpresence or predicts said absence of said microorganism with greaterthan 90%, 95%, 98%, 99%, 99.9%, 99.99% or greater than 99.999%sensitivity.

EMBODIMENT 151. An embodiment comprising: (a) detecting a presence or anabsence of a non-pathogenic microorganism in a sample; (b) predicting,by a computer system, a presence or an absence of a pathogenicmicroorganism in said sample based on said presence or said absence ofsaid non-pathogenic microorganism.

EMBODIMENT 152. The method of embodiment 151, wherein said predicting isperformed by a machine learning algorithm in a computer.

EMBODIMENT 153. The method of embodiment 152, wherein said machinelearning algorithm is selected from the group consisting of: a supportvector machine (SVM), a Naive Bayes classification, a random forest,Logistic Regression, and a neural network.

EMBODIMENT 154. The method of embodiment 152, wherein said sample is afood sample or an environmental sample associated with said food sample.

EMBODIMENT 155. The method of embodiment 154, wherein said food sampleis a perishable.

EMBODIMENT 156. The method of embodiment 155, wherein said perishable isa meat.

EMBODIMENT 157. The method of embodiment 156, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 158. The method of embodiment 155, wherein said perishable isa fruit, an egg, a vegetable, a produce, or a legume.

EMBODIMENT 159. The method of embodiment 154, wherein said environmentalsample is a surface swab or a surface rinse of said environment.

EMBODIMENT 160. The method of embodiment 154, wherein said environmentalsample is a food storage container, a food handling equipment, or apiece of clothing from a worker of said environment associated with saidfood processing facility.

EMBODIMENT 161. The method of embodiment 151, wherein said sample is anon-food sample.

EMBODIMENT 162. The method of embodiment 151, wherein said samplecomprises blood, plasma, urine, tissue, faces, bone marrow, saliva orcerebrospinal fluid.

EMBODIMENT 163. The method of embodiment 151, wherein saidnon-pathogenic microorganism.

EMBODIMENT 164. The method of embodiment 151, wherein saidnon-pathogenic microorganism is selected from the group consisting of:Enterobacter asburiae, Enterobacter bugandensis, Enterobactercancerogenus, Enterobacter cloacae, Enterobacter endosymbiont,Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii,Enterobacter mori, and Enterobacter soli.

EMBODIMENT 165. The method of embodiment 151, wherein said pathogenicmicroorganism is selected from the group consisting of: a microorganismof the Salmonella genus, a microorganism of the Campylobacter genus, amicroorganism of the Listeria genus, and a microorganism of theEscherichia genus.

EMBODIMENT 166. The method of embodiment 151, wherein said pathogenicmicroorganism is selected from the group consisting of Vibrioparahaemolyticus, Vibrio cholera, Vibrio vulnificus, Escherichia coli,Salmonella enterica, Shigella boydii, Campylobacter jejuni,Staphylococcus aureus, Listeria monocytogenes, Clostridium botulinum,Yersinia pseudotuberculosis, Clostridium perfringens, Yersiniaenterocolitica, Coxiella burnetii, Yersinia pseudotuberculosis, Vibrioparahaemolyticus, Bacillus cereus, Mycobacterium tuberculosis, Shigellaflexneri, Shigella boydii, Shigella dysenteriae, and Shigella sonnei.

EMBODIMENT 167. The method of embodiment 151, wherein said detectingcomprises a nucleic acid characterization assay selected from the groupconsisting of a pore sequencing reaction, a next generation sequencingreaction, a shotgun next generation sequencing, Sanger sequencing, orhybridization assay.

EMBODIMENT 168. The method of embodiment 167, wherein said nucleic acidcharacterization assay is a pore sequencing reaction.

EMBODIMENT 169. The method of embodiment 168, wherein said poresequencing reaction distinguishes an epigenetic pattern on a nucleicacid from said food sample or from said environmental sample.

EMBODIMENT 170. The method of embodiment 151, further comprisingperforming an assay to confirm the prediction of (b).

EMBODIMENT 171. The method of embodiment 170, wherein said assay is aserotyping reaction.

EMBODIMENT 172. The method of embodiment 170, wherein said assay is apolymerase chain reaction (PCR) assay.

EMBODIMENT 173. The method of embodiment 172, wherein said assay is anenzyme-linked immunosorbent (ELISA) assay.

EMBODIMENT 174. The method of embodiment 172, wherein said assay is anenzyme-linked fluorescent assay (ELFA) assay.

EMBODIMENT 175. An embodiment comprising: (a) detecting a presence or anabsence of a microorganism in a sample or in a facility associated withsaid sample; and (b) predicting, by a computer system, a risk presentedby said facility based on said presence or said absence of saidmicroorganism.

EMBODIMENT 176. The method of embodiment 175, wherein said sample is afood sample or an environmental sample associated with said food sample.

EMBODIMENT 177. The method of embodiment 176, wherein said food sampleis a perishable.

EMBODIMENT 178. The method of embodiment 177, wherein said perishable isa meat.

EMBODIMENT 179. The method of embodiment 178, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 180. The method of embodiment 177, wherein said perishable isa fruit, an egg, a vegetable, a produce, or a legume.

EMBODIMENT 181. The method of embodiment 176, wherein said environmentalsample is a surface swab or a surface rinse of said environment.

EMBODIMENT 182. The method of embodiment 176, wherein said environmentalsample is a food storage container, a food handling equipment, or apiece of clothing from a worker of said environment associated with saidfood processing facility.

EMBODIMENT 183. The method of embodiment 175, wherein said sample is anon-food sample.

EMBODIMENT 184. The method of embodiment 175, wherein said samplecomprises blood, plasma, urine, tissue, faces, bone marrow, saliva orcerebrospinal fluid.

EMBODIMENT 185. The method of embodiment 175, wherein said facility is afood processing facility.

EMBODIMENT 186. The method of embodiment 175, wherein said facility is ahospital or a clinic.

EMBODIMENT 187. The method of embodiment 175, wherein said methodpredicts said presence or said absence of said microorganism withgreater than 90%, 95%, 98%, 99%, 99.9%, 99.99% or 99.999% sensitivity.

EMBODIMENT 188. The method of embodiment 175, wherein said methodpredicts said presence or said absence of said microorganism withgreater than 90%, 95%, 98%, 99%, 99.9%, 99.99% or 99.999% specificity.

EMBODIMENT 189. The method of embodiment 175, wherein said risk informsan insurance for said facility.

EMBODIMENT 190. The method of embodiment 175, wherein said microorganismis a pathogenic or a non-pathogenic microorganism.

EMBODIMENT 191. The method of embodiment 175, wherein said detectingcomprises a sequencing reaction or a hybridization assay.

EMBODIMENT 192. The method of embodiment 191, wherein said sequencingreaction is selected from the group consisting of a pore sequencingreaction, a next generation sequencing reaction, a shotgun nextgeneration sequencing, Sanger sequencing.

EMBODIMENT 193. The method of embodiment 192, wherein said sequencingreaction is a pore sequencing reaction.

EMBODIMENT 194. The method of embodiment 193, wherein said poresequencing reaction distinguishes an epigenetic pattern on a nucleicacid from said food sample or from said environmental sample.

EMBODIMENT 195. The method of embodiment 175, further comprisingperforming an assay to confirm the prediction of (b).

EMBODIMENT 196. The method of embodiment 195, wherein said assay is aserotyping reaction.

EMBODIMENT 197. The method of embodiment 195, wherein said assay is apolymerase chain reaction (PCR) assay.

EMBODIMENT 198. The method of embodiment 195, wherein said assay is anenzyme-linked immunosorbent (ELISA) assay.

EMBODIMENT 199. The method of embodiment 195, wherein said assay is anenzyme-linked fluorescent assay (ELFA) assay.

EMBODIMENT 200. An embodiment comprising: (a) adding a first barcode toa first plurality of nucleic acid sequences from a sample, therebyproviding a first plurality of barcoded nucleic acid sequences; and (b)performing a first sequencing reaction on said first plurality ofbarcoded nucleic acid sequences, wherein said sequencing reaction isperformed on a sequencing apparatus comprising a flow cell; (c) adding asecond barcode to a second plurality of nucleic acid sequences from asecond sample, thereby providing a second plurality of barcoded nucleicacid sequences; and (d) performing a second sequencing reaction on saidsecond plurality of barcoded nucleic acid sequences, wherein said secondsequencing reaction is performed on said sequencing apparatus comprisingsaid flow cell, thereby reusing said flow cell.

EMBODIMENT 201. The method of embodiment 200, wherein said first barcodeand said second barcode are between 1 nucleotide and 18 nucleotides inlength.

EMBODIMENT 202. The method of embodiment 200, wherein said first barcodeand said second barcode are about 9 nucleotides in length.

EMBODIMENT 203. The method of embodiment 200, wherein said first barcodeand said second barcode have identical sequences.

EMBODIMENT 204. The method of embodiment 200, wherein said first barcodeand said second barcode have distinct sequences.

EMBODIMENT 205. The method of embodiment 200, further comprising addinga third barcode to a third plurality of nucleic acid sequences from athird food sample or from a third environmental sample associated withsaid third food sample, thereby providing a third plurality of barcodednucleic acid sequences.

EMBODIMENT 206. The method of embodiment 205, further comprisingperforming a third sequencing reaction on said third plurality ofbarcoded nucleic acid sequences, wherein said third sequencing reactionis performed on said sequencing apparatus comprising said flow cell,thereby reusing said flow cell for a third time.

EMBODIMENT 207. The method of embodiment 200, wherein said firstbarcode, said second barcode, and said third barcode have identicalsequences.

EMBODIMENT 208. The method of embodiment 200, wherein said firstbarcode, said second barcode, and said third barcode have distinctsequences.

EMBODIMENT 209. The method of embodiment 200, further comprisingperforming an amplification reaction or nucleic acid enrichment on saidplurality of nucleic acid sequences prior to sequencing of (b), (d), orboth.

EMBODIMENT 210. The method of embodiment 200, wherein said sequencing isselected from the group consisting of a pore sequencing reaction, a nextgeneration sequencing reaction, a shotgun next generation sequencing, orSanger sequencing.

EMBODIMENT 211. The method of embodiment 200, wherein said sequencingreaction is a pore sequencing reaction.

EMBODIMENT 212. The method of embodiment 211, wherein said poresequencing reaction distinguishes an epigenetic pattern on a nucleicacid from said food sample or from said environmental sample.

EMBODIMENT 213. The method of embodiment 211, wherein said epigeneticpattern is a methylation pattern.

EMBODIMENT 214. The method of embodiment 200, wherein said plurality ofnucleic acid sequences comprise complementary DNA (cDNA) sequences.

EMBODIMENT 215. The method of embodiment 200, wherein said plurality ofnucleic acid sequences comprise ribonucleic acid (RNA) sequences.

EMBODIMENT 216. The method of embodiment 200, wherein said plurality ofnucleic acid sequences comprise genomic deoxyribonucleic acid (gDNA)sequences.

EMBODIMENT 217. The method of embodiment 200, wherein said plurality ofnucleic acid sequences comprise a mixture of cDNA, RNA, and gDNAsequences.

EMBODIMENT 218. The method of embodiment 200, wherein said first sampleis a first food sample or a first environmental sample associated withsaid first food sample.

EMBODIMENT 219. The method of embodiment 200, wherein said second sampleis a second food sample or a second environmental sample associated withsaid first food sample.

EMBODIMENT 220. The method of embodiments 218 or 219, wherein said firstfood sample, said second food sample, or both are a perishable.

EMBODIMENT 221. The method of embodiment 220, wherein said perishable isa meat.

EMBODIMENT 222. The method of embodiment 221, wherein said meat is apoultry, a red meat, a fish, or a swine.

EMBODIMENT 223. The method of embodiment 221, wherein said perishable isa fruit, an egg, a vegetable, a produce or a legume.

EMBODIMENT 224. The method of embodiments 218 or 219, wherein said firstenvironmental sample, said second environmental sample, or both is asurface swab or a surface rinse of said environment.

EMBODIMENT 225. The method of embodiments 218 or 219, wherein said firstenvironmental sample, said second environmental sample, or both is afood storage container, a food handling equipment, or a piece ofclothing from a worker of said environment associated with said foodprocessing facility.

EMBODIMENT 226. The method of embodiment 200, wherein said sample is anon-food sample.

EMBODIMENT 227. The method of embodiment 226, wherein said samplecomprises blood, plasma, urine, tissue, feces, bone marrow, saliva orcerebrospinal fluid.

EMBODIMENT 228. A nucleic acid sequencing apparatus comprising: (a) anucleic acid library preparation compartment comprising two or morechambers configured to prepare a plurality of nucleic acids from asample for a sequencing reaction, wherein said compartment isoperatively connected to a nucleic acid sequencing chamber; (b) anucleic acid sequencing chamber, wherein said nucleic acid sequencingchamber comprises: (i) one or more flow cells comprising a plurality ofpores or sequencing cartridges configured for the passage of a nucleicacid strand, wherein two or more of the one or more flow cells arejuxtaposed to one another; and (c) an automated platform, wherein saidautomated platform is programmed to robotically move a sample from saidnucleic acid library preparation compartment into said nucleic acidsequencing chamber.

EMBODIMENT 229. The nucleic acid sequencing apparatus of embodiment 228,wherein said automated platform moves a second sample from said nucleicacid library preparation compartment or from previously failedsequencing chamber into said nucleic acid sequencing chamber upondetecting a failure of a sequencing reaction.

EMBODIMENT 230. The nucleic acid sequencing apparatus of embodiment 228,wherein said automated platform moves a second sample from said nucleicacid library preparation compartment into said nucleic acid sequencingchamber upon detecting a completion of a sequencing reaction.

EMBODIMENT 231. The nucleic acid sequencing apparatus of embodiment 228,further comprising adding a barcode to a plurality of nucleic acids insaid two or more chambers of (a), thereby providing a plurality ofbarcoded nucleic acids for said sequencing reaction.

EMBODIMENT 232. The nucleic acid sequencing apparatus of embodiment 228,further comprising adding a plurality of mutually exclusive barcodes toa plurality of nucleic acids in said two or more chambers of (a),thereby providing a plurality of mutually exclusive barcoded nucleicacids.

EMBODIMENT 233. The nucleic acid sequencing apparatus of embodiment 232,wherein said automated platform robotically moves two or more of saidmutually exclusive barcoded nucleic acids into said nucleic acidsequencing chamber.

EMBODIMENT 234. The nucleic acid sequencing apparatus of embodiment 232,wherein said automated platform robotically moves two or more of saidmutually exclusive barcoded nucleic acids into a same flow cell of saidone or more flow cells.

EMBODIMENT 235. The nucleic acid sequencing apparatus of embodiment 232,wherein said sample is a food or an environmental sample.

EMBODIMENT 236. The nucleic acid sequencing apparatus of embodiment 232,wherein said sample is a non-food sample.

EMBODIMENT 237. The nucleic acid sequencing apparatus of embodiment 236,wherein said sample comprise blood, plasma, urine, tissue, faces, bonemarrow, saliva or cerebrospinal fluid.

EMBODIMENT 238. An embodiment comprising: (a) adding a first molecularindex to a first plurality of nucleic acid sequences from a sample,thereby providing a first plurality of indexed nucleic acid sequences;and (b) adding a second molecular index to said first plurality ofnucleic acid sequences from said first sample, thereby providing asecond plurality of indexed nucleic acid sequences; and (c) adding athird molecular index to said first plurality of nucleic acid sequencesfrom said first sample, thereby providing a third plurality of indexednucleic acid sequences; (d) performing a sequencing reaction on saidthird plurality of nucleic acid sequences; and (e) demultiplexing, by acomputer system, said third plurality of nucleic acid sequencescomprising said first molecular index, said second molecular index, andsaid third molecular index.

EMBODIMENT 239. The method of embodiment 238, wherein said firstmolecular index, said second molecular index, and said third molecularindex are between 1 nucleotide and 18 nucleotides in length.

EMBODIMENT 240. The method of embodiment 238, herein said firstmolecular index, said second molecular index, and said third molecularindex are about 9 nucleotides in length.

EMBODIMENT 241. The method of embodiment 238, wherein said firstmolecular index, said second molecular index, and said third molecularindex have identical sequences.

EMBODIMENT 242. The method of embodiment 238, wherein said firstmolecular index, said second molecular index, and said third molecularindex have distinct sequences.

EMBODIMENT 243. The method of embodiment 238, wherein said firstplurality of indexed nucleic acid sequences, said second plurality ofindexed nucleic acid sequences, and said third plurality of indexednucleic acid sequences form a barcode comprising a periodic blockdesign.

EMBODIMENT 244. The method of embodiment 243, wherein said periodicblock design has a defined Levenshtein distance between each of saidfirst plurality of indexed nucleic acid sequences, said second pluralityof indexed nucleic acid sequences, and said third plurality of indexednucleic acid sequences.

EMBODIMENT 245. The method of embodiment 238, wherein said firstplurality of indexed nucleic acid sequences, said second plurality ofindexed nucleic acid sequences, and said third plurality of indexednucleic acid sequences form a barcode comprising a nonperiodic blockdesign.

EMBODIMENT 246. The method of embodiment 245, wherein said nonperiodicblock design has a defined Levenshtein distance between each of saidfirst plurality of indexed nucleic acid sequences, said second pluralityof indexed nucleic acid sequences, and said third plurality of indexednucleic acid sequences.

EMBODIMENT 247. The method of embodiment 246, wherein said Levenshteindistance between each of said first plurality of indexed nucleic acidsequences, said second plurality of indexed nucleic acid sequences, andsaid third plurality of indexed nucleic acid sequences is the maximumpossible Levenshtein distance.

EMBODIMENT 248. An automatable microfluidic device for analysing a testliquid comprising: a sensor provided in a sensing chamber; a flow pathcomprising a sensing chamber inlet and a sensing chamber outletconnecting to the sensing chamber for respectively passing liquid intoand out of the sensing chamber, and a sample input port in fluidcommunication with the inlet; a liquid collection channel downstream ofthe outlet; a flow path interruption between the sensing chamber outletand the liquid collection channel, preventing liquid from flowing intothe liquid collection channel from upstream, whereby the device may beactivated by completing the flow path between the sample input port andthe liquid collection channel; a conditioning liquid filling from thesample input port to the flow path interruption such that the sensor iscovered by liquid and unexposed to a gas or gas/liquid interface;wherein the device is configured such that following activation of thedevice, the sensor remains unexposed to a gas or gas/liquid interfaceand the application of respectively one or more volumes of test liquidto a wet surface of the input port provides a net driving forcesufficient to introduce the one or more volumes of test liquid into thedevice and displace buffer liquid into the liquid collection channel,wherein the device further comprises a removable seal for the sampleinput port, wherein the removable seal has a body that projects at least1 cm above the surface of the microfluidic device when seated in thesample input port.

EMBODIMENT 249. The device of embodiment 248, wherein the removable sealprojects at least 1, 2.0, 2.5, 3, or 3.5 cm above the surface of thedevice.

EMBODIMENT 250. The device of embodiment 248, wherein the removable sealis cylindrical, with a first flat end and a second tapered end thattapers to a size sufficient to plug the sample input port on the device.

EMBODIMENT 251. The device of embodiment 248, wherein the removable sealcomprises a metallic material.

EMBODIMENT 252. The device of embodiment 249, wherein the removable sealcomprises tungsten, aluminum, austenic stainless steel, or ferriticstainless steel.

EMBODIMENT 253. The device of embodiment 249, wherein the removable sealis resistant to decontamination in dilute nitric acid, 1M NaOH, ordilute sodium hypochlorite.

EMBODIMENT 254. The device of embodiment 249, wherein the removable sealcomprises polypropylene or polycarbonate.

EMBODIMENT 255. The nucleic acid sequencing apparatus of embodiment 228,wherein the one or more flow cells comprising a plurality of pores orsequencing cartridges is the automatable microfluidic device of any oneof claims 248-255.

EXAMPLES Example 1 Preparation of Food and Environmental Samples

Food and environmental samples may be processed for various purposes,such as the enrichment of one or more microorganism from the sample, orthe isolation of one or more microorganism from the sample. Thefollowing protocol was used in the preparation of various food andenvironmental samples including: carcass rinses, stainless steel,primary production boot covers, dry pet food and shell eggs.

TABLE 1 Food and Environmental Sample Preparation Table 1: Food andEnvironmental Sample Preparation Enrichment Amount determined by volumeor Matrix Sample Size weight Incubation Carcass Rinse 30 ± 0.6 mL samplerinse fluid 20 ± 0.5 mL of Clear 42 ± 1° C. for Salmonella media (CSM)9-24 h Stainless Steel 1 sponge pre moistened with 10 10 ± 0.5 mL Clear42 ± 1° C. for mL tris-buffered saline Salmonella media (CSM) 9-24 hEnvironmental 1 environmental sampling bootie 50 ± 1 mL Clear 42 ± 1° C.for Boot Cover pre-moistened with 10 mL skim Salmonella media (CSM) 9-24h milk Pet Food 25 ± 0.5 g 100 ± 1 mL Clear 42 ± 1° C. for Salmonellamedia (CSM) 9-24 h Shell Eggs  100 ± 2 g 200 ± 2 mL Clear 42 ± 1° C. forSalmonella media (CSM) 9-24 h

Example 2 Obtaining a Carcass Food Sample

In this example, carcass food samples are generated by asepticallydraining excess fluid from a carcass and transferring the carcass to alarge sterile sampling bag. 100 mL of an enriched broth, in this case,Clear Salmonella media (CSM) was poured into the cavity of the carcassin the sampling bag. The carcass was rinsed inside and out with arocking motion for about one minute, while assuring that all surfaces(interior and exterior of the carcass) were rinsed. About 20±0.5 mL ofthe CSM was added to the sample bag and homogenized by massaging samplebag for approximately 1.5-2 min. The sample was incubated at 42±1° C.for 9-24 h, providing an enriched sample.

Example 3 Obtaining an Environmental Sample from a Stainless SteelSurface

In this example, a stainless steel surface environmental sample wasgenerated by moistening a sterile sampling sponge in 10 mL of Dey-EngleyBroth prior to sampling, or using a sponge pre-moistened in the same.The sponge was used to touch, scrub, or otherwise contact the stainlesssteel surface and it was subsequently placed into a sampling bag. About10±0.5 mL of CSM was added to the sampling sponge. Subsequently, thesponge was pressed to expel the collection broth into the CSM solution.The sample was incubated at 42±1° C. for 9-24 h, providing an enrichedsample.

Example 4 Obtaining an Environmental Sample from a Boot Cover

In this example, an environmental sample from a boot cover was firstpre-moistened in skim milk. About 50±1 mL of CSM was then added to thesampling bag containing boot cover environmental sample. The contentswere mixed thoroughly for approximately 1.5-2 min, and incubated at42±1° C. for 9-24 h, thereby providing an enriched sample. The enrichedsample was removed from incubator and briefly mixed.

Example 5 Obtaining a Pet Food Sample

In this example, about 25±0.5 g of a pet food sample were added into afiltered sampling bag. About 100±1 mL CSM was then added to the samplingbag containing said pet food. The contents were mixed thoroughly forapproximately 1.5-2 min, and incubated at 42±1° C. for 9-24 h, therebyproviding an enriched sample. The enriched sample was removed fromincubator and briefly mixed.

Example 6 Obtaining a Shell Egg Food Sample

In this example, about 100±2 g of a homogenized egg sample was added toa filtered sampling bag. About 200±2 mL CSM was then added to thesampling bag containing said homogenized egg sample. The contents weremixed thoroughly for approximately 1.5-2 min, and incubated at 42±1° C.for 9-24 h, thereby providing an enriched sample. The enriched samplewas removed from incubator and briefly mixed.

Example 7 Photoreactive DNA-Binding Dye Treatment

In this example, a photoreactive DNA-binding dye, namely propidiummonoazide (PMA) was added to various food and environmental samples,including the samples described in Examples 1-6. In general, 5 μL of aPMAxx solution was added to a well in a 200 μL 96-well PCR plate.Approximately 45 μL of each enriched sample from the sampling bagsdescribed in Examples 1-6 was added to individual wells in PCR platecontaining PMAxx. The samples were mixed thoroughly by gentle pipettingand placed in the dark for 10 min at room temperature. Subsequently, theplates were incubated under a blue LED light for 20 min. 10 μL of eachsample were then diluted with 90 μL of Lysis Buffer in a new 200 μL96-well PCR plate. The plate was then incubated in a thermocycler asshown below. Alternatively the sample could have been incubated in awater bath.

Step Temperature Time 1 37° C. 20 min 2 95° C. 10 min

Example 8 PMAxx-Induced Removal of Free-Floating DNA

This example demonstrates that addition of a solution of thephotoreactive DNA-binding dye PMAxx to a sample solution reduced thenumber of free-floating and contaminating DNA in said sample.Specifically, 45 μL of each enriched sample from the sampling bags asdescribed in Examples 1-7 was added to individual wells of the 96-wellPCR plate containing 25 μL of PMAxx solution. The sample solutions weremixed thoroughly by gentle pipetting and placed in the dark for 10 minat room temperature. Subsequently, the plates were incubated under ablue LED light for 20 min. 10 μL of each sample were then diluted with90 μL of Lysis Buffer in a new 200 μL 96-well PCR plate. The plate wasthen incubated in a thermocycler as shown below. Analysis of the samplereadouts showed that the addition of PMAxx solution (25 μL) to thesample solution was sufficient to reduce the number of free-floating DNAby at least 2 orders of magnitude, as shown in FIG. 13 .

Example 9 Amplification Reaction

In this example, the samples described in Examples 1-8 were subjected toan amplification reaction. Briefly 15 μL of primer cocktail andpolymerase master mix was added to individual wells of an empty 200 μL96-well PCR plate. About 5 μl of each sample treated with aphotoreactive DNA-binding dye treatment was added to the respectivewells containing the polymerase master mix. The solution was mixedgently by pipetting up and down and placed in a thermocycler with theconditions described below.

Step Temperature Time 1 95° C. 3 min 2 95° C. 30 sec 3 57° C. 1 min 472° C. 1 min 5 Go to step 2, 37 times 6 72° C. 10 min 7 10° C. Hold

Example 10 Library Preparation

In this example, Solid Phase Reversible Immobilization (SPRI) MagneticBeads were used to purify and quantify one or more of the samplesdescribed in Examples 1-9. Briefly, the SPRI beads were removed from 4°C. storage and allowed to reach room temperature for approximately 15min. About 1 mL of 80% ethanol was prepared by combining 800 μL ofethanol and 200 μL of molecular biology grade water. Equal volumes ofeach samples amplification product (described in Example 9) was used toobtain at least 100 μL of pooled products, which was purified using theSPRI beads along with standard manufacturing protocols. Briefly, 100 μLof vortexed, pooled PCR product was pipetted into a 0.2 mL PCR tube andadd 60 μL of SPRI beads. The tube was mixed thoroughly by pipetting upand down approximately 10 times and incubated at room temperature for 5min. The sample/bead mixture was placed in a magnetic stand and thebeads were allowed to pellet in a ring for approximately 30-60 s,leaving a clear supernatant. The supernatant was discarded by leavingthe tube in the magnetic stand while placing the pipette tip to thebottom center of the tube when aspirating to avoid disturbing the beads.190 μL of 80% ethanol was then added to the tube, and incubated for 5-10s. The tube was aspirated fully and the ethanol solution discarded. Theprocess was repeated twice. The sample was allowed to dry for 3-5 min atroom temperature, or until no visible ethanol remained. Once thoroughlydry, the tube was removed from the magnetic stand and re-suspended in 50μL of 10 mM RSB into the tube. The tube was mixed thoroughly by gentlypipetting up and down approximately 10 times and incubate at roomtemperature for 2 min. The tube was moved to a magnetic stand andincubated at room temperature for 2 min to allow the beads to pellet. 50μL of the eluate was removed and retained.

Example 11 End Repair

In this example, the terminal ends of fragment nucleic acids describedin Example 10 were repaired as described below. First, the followingreagents were combined and mixed well by pipetting up and downapproximately 10 times.

Reagent Volume Purified Pooled Libraries 45 μL NEB Ultra II end-prepreaction buffer 7 μL NEB Ultra II End-prep enzyme mix 3 μL ONT DNA CS(DCS) 5 μL Total 60 μL

The samples were then spun for approximately 5 s using a benchtopminifuge. End-repair was performed in a thermal cycler with thefollowing conditions:

Step Temperature Time 1 20° C. 5 min 2 65° C. 5 min 3 25° C. 5 min

Subsequently, the samples were spun for approximately 5 s using abenchtop minifuge. 60 μL of SPRI beads were added to the end-repairedproduct and mixed by pipetting up and down approximately 10 times. Thesamples were incubated for 5 min at room temperature. The sample/beadmixture was placed in a magnetic stand and the beads were allowed topellet in a ring around the middle portion of the tube for approximately30-60 s, leaving a clear supernatant. The supernatant was discarded byleaving the tube in the magnetic stand while placing the pipette tip tothe bottom center of the tube when aspirating to avoid disturbing thebeads. 190 μL of 80% ethanol was added to the samples. The 80% ethanolsolution was incubated in the tube for 5-10 s, and the ethanol wasaspirated and discarded. This process was repeated twice. The sample wasallowed to dry for 5 min at room temperature, or until no visibleethanol remained. The beads were resuspended with 31 μL molecularbiology grade water and mixed by gently pipetting up and downapproximately 10 times and incubate for 2 min at room temperature. Thetube was moved to a magnetic stand and the beads were allowed to pelletfor approximately 30-60 s. The eluate was retained as the “end-repairedproduct”.

Example 12 Ligation

In this example, using the end-repaired product of Example 11, thefollowing reagents were combined:

Reagent Volume End-repaired product 30 μL ONT Adapter Mix (AMX 1D) 20 μLNEB Blunt/TA Ligase Master Mix 50 μL Total 100 μL

The reagents were gently mixed by pipetting up and down approximately 10times and were incubated at room temperature for 10 min. About 40 μL ofSPRI beads were added to the mixture, gently mixed, and incubated atroom temperature for 5 min. The sample/bead mixture was placed in amagnetic stand and the beads were allowed to pellet in a ring around themiddle portion of the tube for approximately 30-60 s, leaving a clearsupernatant. The supernatant was discarded by leaving the tube in themagnetic stand while placing the pipette tip to the bottom center of thetube when aspirating to avoid disturbing the beads. The tube was removedfrom the magnetic rack and 140 μL of ONT-Adapter Bead Binding buffer waspipetted onto the beads. The sample was mixed by gently pipetting up anddown approximately 10 times to resuspend the pellet. The tube wasreturned to the magnetic stand and the beads were allowed to pellet in aring around the middle portion of the tube for approximately 30-60 s,leaving a clear supernatant. The supernatant was discarded by leavingthe tube in the magnetic stand while placing the pipette tip to thebottom center of the tube when aspirating to avoid disturbing the beads.The tube was removed from the magnetic rack and an additional 140 μL ofAdapter Bead Binding buffer was added and pipetted up and down toresuspend the pellet. The sample/bead mixture was placed in a magneticstand and the beads were allowed to pellet into a ring around the middleportion of the tube for approximately 30-60 s, leaving a clearsupernatant. The supernatant was discarded by leaving the tube in themagnetic stand while placing the pipette tip to the bottom center of thetube when aspirating to avoid disturbing the beads. The tube was thenremoved from the magnetic stand. About 15 μL of Elution Buffer (ELB) wasadded to the beads, and the beads were mixed thoroughly by pipetting upand down approximately 10 times and incubate for 10 minutes at roomtemperature for 5 min. The tubes were moved to a magnetic stand and thebeads allowed to pellet for approximately 30-60 s. About 15 μL of eluatewas remove and retained as the “final ligated product” for sequencing.

Example 13 Pore Sequencing

In this example, a food or an environmental sample was processed by poresequencing using standard manufacturer protocols. Briefly, one or moreflow cells were primed by combining the following reagents per flowcell:

Reagent Volume ONT-Running Buffer with Fuel Mix (RBF) 480 μL Moleculargrade H₂O 520 μL Total 1,000 μL

A loading library was prepared by combining the following reagents:

Reagent Volume ONT-Running Buffer with Fuel Mix (RBF) 35 μL ONT-LibraryLoading Beads (LLB) 25.5 μL Final ligated product 12 μL Molecular gradeH₂O 2.5 μL Total 75 μL

The priming port on the Flow Cell was gently opened and approximately 50μL of the preservative buffer and any small bubbles were removed, asillustrated by FIG. 14 . About 800 μL of the priming mix was added intothe priming port of the Flow Cell. Subsequently, 200 μL of the primingmix was dispensed into the Priming port. The final loading library wasmixed thoroughly and 75 μL were added into the SpotON port, asillustrated by FIG. 15 . The lid of the pore sequencing device wasclosed and the sequencing was executed.

Example 14 Data Analysis and Interpretation

In this example, an electronic communication comprising a data setassociated with the sequencing reaction described in Example 13 wastransmitted over the cloud for analysis. The results of the analysiswere reported back to customer. FIG. 16 in this particular example, thecustomer requested an analysis of the sample for the presence or absenceof Listeria, Salmonella, Campylobacter, and E. coli, which required thesimultaneous targeting of multiple pathogens.

Example 15 Identification of a Microorganism in a Food, EnvironmentalSample, or in a Non-Food Associated Sample by Microbiome Metagenomicsand Supervised Learning

In this example, data from pore sequencing was used to identifyfoodborne disease-causing microorganisms. Briefly, the methods andprocesses described in Examples 1-13 were used to identify food orenvironmental samples comprising one or more of the organism shownbelow.

TABLE 2 Table 2: Exemplary Pathogenic Microorganisms Identified byMethods According to This Disclosure Onset Common Name Time After Signs& Duration Organism of Illness Ingesting Symptoms of Ilness Food SourcesBacillus B. cereus food 10-16 hrs Abdominal 24-48 hours Meats, stews,cereus poisoning cramps, watery gravies, vanilla diarrhea, nausea sauceCampylobacter Campylobacteriosis 2-5 days Diarrhea, cramps, 2-10 daysRaw and jejuni fever, and undercooked vomiting; diarrhea poultry, may bebloody unpasteurized milk, contaminated water Clostridium Botulism 12-72hours Vomiting, Variable Improperly botulinum diarrhea, blurred cannedfoods, vision, double especially vision, difficulty home-canned inswallowing, vegetables, muscle weakness. fermented fish, Can result inbaked potatoes respiratory failure in aluminum and death foilPerfringens Perfringens food 8-16 hours Intense abdominal Usually Meats,poultry, poisoning cramps, watery 24 hours gravy, dried or diarrheaprecooked foods, time and/or temperature- abused foods CryptosporidiumIntestinal 2-10 days Diarrhea (usually May be Uncooked foodcryptosporidiosis watery), stomach remitting and or food cramps, upsetrelapsing over contaminated stomach, slight weeks to by an ill foodfever months handler after cooking, contaminated drinking waterCyclospora Cyclosporiasis 1-14 days, Diarrhea (usually May be Varioustypes cayetanensis usually at watery), loss of remitting and of freshleast 1 appetite, relapsing over produce week substantial loss of weeksto (imported weight, stomach months berries, lettuce, cramps, nausea,basil) vomiting, fatigue E. coli E. coli infection 1-3 days Waterydiarrhea, 3-7 or Water or food (Escherichia (common cause of abdominalmore days contaminated coli) “travelers' cramps, some with humanproducing diarrhea”) vomiting feces toxin E. coli Hemorrhagic 1-8 daysSevere (often 5-10 days Undercooked O157:H7 colitis or bloody) diarrhea,beef (especially E. coli O157:H7 abdominal pain hamburger), infectionand vomiting. unpasteurized Usually, little or milk and juice, no feveris raw fruits and present. More vegetables (e.g. common in sprouts), andchildren 4 years contaminated or younger. Can water lead to kidneyfailure. Hepatitis A Hepatitis 28 days Diarrhea, dark Variable, Rawproduce, average urine, jaundice, 2 weeks-3 months contaminated (15-50days) and flu-like drinking water, symptoms, i.e., uncooked fever,headache, foods and nausea, and cooked foods abdominal pain that are notreheated after contact with an infected food handler; shellfish fromcontaminated waters Listeria Listeriosis 9-48 hrs for Fever, muscleVariable Unpasteurized monocytogenes gastro- aches, and nausea milk,soft intestinal or diarrhea. cheeses symptoms, Pregnant women made with2-6 weeks may have mild unpasteurized for invasive flu-like illness,milk, ready-to- disease and infection can eat deli meats lead topremature delivery or stillbirth. The elderly or immuno- compromisedpatients may develop bacteremia or meningitis. Noroviruses Variouslycalled 12-48 hrs Nausea, vomiting, 12-60 hrs Raw produce, viralabdominal contaminated gastroenteritis, cramping, drinking water, winterdiarrhea, diarrhea, fever, uncooked acute non- bacterial headache. foodsand gastroenteritis, Diarrhea is more cooked foods food poisoning,prevalent in that are not and food infection adults, vomiting reheatedafter more common in contact with an children. infected food handler;shellfish from contaminated waters Salmonella Salmonellosis 6-48 hoursDiarrhea, fever, 4-7 days Eggs, poultry, abdominal meat, cramps,vomiting unpasteurized milk or juice, cheese, contaminated raw fruitsand vegetables Shigella Shigellosis or 4-7 days Abdominal 24-48 hrs Rawproduce, Bacillary dysentery cramps, fever, and contaminated diarrhea.Stools drinking water, may contain blood uncooked and mucus. foods andcooked foods that are not reheated after contact with an infected foodhandler Staphylococcus Staphylococcal 1-6 hours Sudden onset of 24-48hours Unrefrigerated aureus food poisoning severe nausea and orimproperly vomiting. refrigerated Abdominal meats, potato cramps.Diarrhea and egg salads, and fever may be cream pastries present. VibrioV. 4-96 hours Watery 2-5 days Undercooked parahaemolyticusparahaemolyticus (occasionally or raw seafood, infection bloody)diarrhea, such as abdominal shellfish cramps, nausea, vomiting, feverVibrio V. vulnificus 1-7 days Vomiting, 2-8 days Undercooked vulnificusinfection diarrhea, or raw seafood, abdominal pain, such as blood borneshellfish infection. Fever, (especially bleeding within oysters) theskin, ulcers requiring surgical removal. Can be fatal to persons withliver disease or weakened immune systems.

First, a database was constructed using data from approximately 35,000food or environmental samples (of which about 10% contained traces ofpathogenic microorganisms as shown in Table 3) using two components:microorganism presence and chemical composition. Pore sequencing incombination with use of characteristic polymorphic gene regions(comprising SNP's, RFLP's, STRs, VNTR's, hypervariable regions,minisatellites, dinucleotide repeats, trinucleotide repeats,tetranucleotide repeats, simple sequence repeats, indels, and insertionelements) associated with a wide diversity of microorganisms were usedto analyze each sample for the presence or absence of 17,800 differentbacterial species (representing both pathogenic and non-pathogenicbacterial species). Additionally, data on sample composition wascollected for 4,600 food ingredients in each environmental/food sample.

The data using the top bacteria associated with pathogen contamination(exemplified in FIG. 5 ) was used to train a classification model, whichwas tested for overfitting by machine learning techniques.

We further tested the performance of the model by testing a set ofunknown food or environmental samples (50% of each). The full results ofand ROC analysis of accuracy and precision of the classification modelsare presented in Table 3. In the cases of all the pathogens in Table 3,the metagenomics-based classification model had higher than 95%precision and 97% accuracy for pathogen detection.

TABLE 3 Table 3: Independent Validation of Pathogen Prediction inUnknown Samples Accuracy Precision Pathogen Score Score Vibrioparahaemolyticus 99.78% 96.55% Staphylococcus aureus 99.67% 100.00%Yersinia pseudotuberculosis 99.45% 100.00% Vibrio vulnificus 99.12%100.00% Shigella boydii 99.12% 100.00% Salmonella enterica 96.16% 94.39%Escherichia coli 97.48% 98.40%

Example 16 In Silico Evaluation of Primer Sensitivity and Specificity

This example describes the in silico evaluation of primer sensitivityand specificity for pathogen detection in PCR assays. First, a candidateprimer pair was mapped against inclusion and exclusion sequences insequence databases. Secondly, the identified hits are tabulated based onpredicted amplification patterns in order to then determine thesensitivity and specificity of the primer pair in silico.

Specifically, a primer pair was designed to target Salmonella Montevideoand Salmonella Oranienburg. The composition of the sequence database forin silico evaluation contained 7705 Salmonella genomes, including 98Montevideo/Oranienburg genomes, and 1707 non-Salmonella genomes (totalof 9412 genomes). Tabulation of the analysis results showed that theexact number of 98 Salmonella Montevideo and Oranienburg genomes wasidentified as true positive hits. The remaining 9314 (which equals thetotal number of 9412 genomes minus the 98 true positive hits identified)genomes were characterized as true negative results. The results areshown in FIG. 17 .

Example 17 Reuse of Flow Cells

This example shows that the MinION/GridION flow cell can be reused forsequence sample analysis for at least 2 times. Between each sampleanalysis (50 samples analyzed in each analysis) the flow cell was washedwith a buffer system resulting in 30,000 reads and 26,000 reads persample during the second and third reuse, respectively, compared to36,000 reads per sample when using a new flow cell (FIG. 18 ). FIG. 19illustrates that the number of reads per sample for reusedMinION/GridION flow cells was well above the acceptable minimumthreshold of 10,000 (10 K) reads per sample.

Example 19 Automated Pathogen Risk Detection

A significant source of confounding data in pathogen risk detection iscontamination of samples by resident microorganisms on human handlers.Accordingly, we deployed a biomek-based sample sequencing platform thatrequires no human handling after enrichment (see FIG. 11 and FIG. 12 )to implement the methods of Examples 10-13 and 15. Automation includedevery step of library preparation post incubation of the samples as inExamples 1-6, and included cell lysis, PCR, clean up, and sequencing. Anautomated handling system is illustrated in FIG. 11 .

To determine the performance of our automated handling system, weanalyzed samples spiked with 10 different Salmonella serotypes(Enteritidis, Thyphimurium, I 4_[5]_12:i:-, Newport, Javiana, Infantis,Montevideo, Heidelberg, Muenchen) by automated or manual handling. Theresults are presented in FIG. 20 . Serotype detection accorded 100%between manual and automatic handling, and a student's T-test of thenumber of sequencing reads generated indicated no significant differencebetween manual and automated handling.

Example 20 Detection of Food Product Expiration/Shelf Life by MicrobiomeMetagenomics

A significant limitation of existing environmental pathogen detectionmethods is that they involve culturing, which involves the use ofmultiple different specialized media to detect different classes ofpathogens (e.g. bacteria autotrophic for one or more nutrient vs thosenot). This severely limits the ability to detect food contaminationduring storage. Accordingly, we applied our environmental sampling/poresequencing technique as outlined in Examples 1-13 on 100 samples ofchicken wings and 100 samples of ground chicken. Each sample wasanalyzed for the presence/absence of 17,800 pathogenic andnon-pathogenic bacteria.

We applied a principle components analysis to the whole or groundchicken data sets, which is presented in FIG. 21 and FIG. 22 . Datapoints for both whole and ground chicken samples cluster along adiscernable trajectory more than 2 days prior to their expiration date(see movement along PC2 in the whole chicken sample and PC1/PC3 in theground chicken sample), while data points 1-2 days from expiration beginto rapidly diverge.

The principle components analysis suggested a classification model couldbe built to detect whether or not a whole or ground chicken sample hadexpired. The data on the presence/absence of 17,800 pathogenic andnon-pathogenic bacteria was used to generate a classification model.When tested on an independent data set of samples, this classifiershowed 97% accuracy in detecting samples past their expiration dateusing an ROC analysis.

Example 21 Comparison of Periodic and Nonperiodic Block Design forSequencing Sample Barcodes; Reduction of Crosstalk Using Non-PeriodicBlock Primer Design

To improve detection of desired sequences during sequencing runs, wetested the performance of different barcoding designs on sequencedetection. We generated unique sequences of nucleotides with maximumLevenschtein distances from each other and used them to generate twoformats of barcodes to be applied to sequences during librarypreparation: a) a periodic block design, in which each barcode consistedof a unique block sequence repeated 3 times, and b) a nonperiodic blockdesign, in which 3 unique blocks were combined in tandem for eachbarcode sequence.

We tested these nonperiodic and periodic block designs alongside aconventional barcode design (which were designed barcodes provided byour sequencing platform provider) when applied to the same samples intest sequencing runs (see FIG. 23 ). Briefly, a defined Levenshteindistance between each “building block” or molecular index can be used toform larger barcodes. Such larger barcodes can have a period blockdesign, such as barcodes created by repeating each block multiple timeswith the largest possible Levenshtein distance between the individualblocks (see FIG. 23 ). Alternatively, such barcodes can also have anonperiodic block design, such as barcodes created by concatenativemultiple blocks that are unique to each barcode with the largestpossible Levenshtein distance between the individual blocks (see FIG. 23).

We performed 10 ONT MinION runs and averaged the % of retained sequencesand crosstalk for each run. The results are presented in Table 4. Bothperiodic and nonperiodic barcode designs showed improvements inretention and crosstalk versus the conventional design, with thenonperiodic design being the best in both metrics.

Both barcode designs present distinct advantages. Both increase thenumber of retained sequences and allow for adjustable precision bychoosing 1, 2, or 3 blocks in demultiplexing, but the periodic designrequires fewer repeat blocks and presents less complexity indemultiplexing, whereas the nonperiodic design allows for improvedcrosstalk prevention. The improved crosstalk prevention of thenonperiodic design suggests a method of reducing crosstalk during highlymultiplexed runs or when a flowcell is reused.

TABLE 4 Table 4: Performance of Conventional Barcode Design vs Periodicand Nonperiodic Block Designs Conventional Periodic Block NonperiodicDesign Design Block Design Retained Sequences 85% 96% 98% Crosstalk  6% 5%  2%

Example 22 Detection of Transient vs Resident Microbes by Metagenomics

Listeria-containing food and environmental samples were prepared,libraries were constructed, and sequencing was performed as in Examples1-13 and 15. Samples were analyzed for the presence of Listeria byanalyzing highly polymorphic genetic markers. A principle componentanalysis of the Listeria sequences isolated from sequencing (see FIG. 24) identified clusters of closely related bacteria which likelyoriginated from the same source.

Example 23 Detection of Microbial Serotype Early in Sequencing Run

The length of time for a full sequencing run represents a majorlimitation in the speed of detection or serotyping of pathogenicbacterial strains by high-throughput sequencing. We hypothesized thatusing “live” detection calls during sequencing runs (which can beperformed as early as 1 hour for ONT MinION and GridION, and 5 hours forIllumina MiSeq) would allow for certain bacteria to bedetected/serotyped on a preliminary basis based on sequencing, withfollow-up confirmation by other non-sequencing-based tests (e.g. Q-PCR).

We performed a test analysis of 50 environmental samples with about 15%positive for one of the pathogens identified in Table 3; positivesamples were spiked with Salmonella, Listeria, E. coli, andCampylobacter (2 samples each) from the top known pathogenic topstrain/serotypes. Pathogen species was detected by detection ofcharacteristic genomic markers. We compared the accuracy of speciesdetection and serotyping at “live” and complete timepoints for thesequencing runs. The results are presented in Table 5. Early detection(1 hour for ONT MinION, and 5 hours for Illumina MiSeq) was 100%accurate for both formats, while MinION showed improved accuracy forserotyping.

TABLE 5 Table 5: “Early call” Detection of Bacterial Species andSerotype Sequences at Detection Serotyping Final Platform early callcalls calls serotyping call MiSeq 425,000 100% 20% 100% MinION 630,000100% 60% 100%

Example 24 Cell Concentration from Prepared Food or EnvironmentalMicrobial Samples

Food or environmental samples of microbes prepared as in Examples 1-6are ideally subjected to a concentration step to maximize theconcentration of pathogen associated nucleic acids (e.g. represented inCFU/μl) and improve downstream detection by sequencing. A filter-freemethod involving phase separation is used to maximize throughput insample preparation.

Briefly, a small volume of a liquid formulation that is designed to beto a) not be miscible with the enrichment media; b) possess a density ofmass similar to that of the desired cell type; c) be unreactive withdownstream applications; d) spontaneously separate into a distinct layerafter mixing with the enrichment media output from the processes ofExamples 1-6 is added to the enrichment media, and the sample is allowedto equilibrate in a conical tube to reach a state shown in FIG. 27 ,which illustrates the process with microbeads instead of cells. In someembodiments, the equilibration occurs with or withoutcentrifugation-assisted phase separation. The aqueous liquid formulationadded can contain a mixture of polymers capable of formingstep-gradients in density (e.g. Ficoll, PEG, glycerol). The desired cellmaterial (e.g. microbeads shown in FIG. 27 ), is then collected bydirectly pipetting the desired layer and collecting it via aflow-fraction collection method.

Example 25 Re-Using Flow Cells

3 groups of 96 samples (including a mixture of samples either targetpathogen positive as positive samples or non-target pathogen as negativesamples) were prepared according to the methods described in examples7-12. Samples were barcoded by transfer of the libraries to 96-wellplates containing a uniquely indexed barcode specific to each well ofthe 96-well plate. Each group of samples from the 96 well plates werepooled into a single solution and each sample was run successively on anOxford Nanopore flow cell. Each cell was washed with buffer in betweenthe runs. Different numbers of Salmonella-positive and -negative sampleswere provided between the runs to introduce sequence variety into eachgroup. These samples were apportioned into different wells ofbarcode-indexed plates. The index plates and barcode assignments foreach group are presented in the table below. Tables 6-8 illustrate on a96 well grid the sample assignment (positive or negative) to eachwell/unique barcode index for each of the 3 successive runs.

Table 6 illustrates index and well assignments of positive and negativeSalmonella samples for each run on the same nanopore flow cell. Table 6illustrates a first run, plate IP1.

TABLE 6 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + + + + + + B − − − − −− − − − − − − C + + + + + + + + + + + + D + + + + + + + + + + + +E + + + + + + + + + + + + F + + + + + + + + + + + + G − − − − − − − − −− − − H + + + + + + + + + + + +

Table 7 illustrates a 2nd run, plate IP2.

TABLE 7 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + + + + + +B + + + + + + + + + + + + C − − − − − − − − − − − − D − − − − − − − − −− − − E − − − − − − − − − − − − F − − − − − − − − − − − −G + + + + + + + + + + + + H + + + + + + + + + + + +

Table 8 illustrates a 3rd run, plate IP3.

TABLE 8 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + + + + + +B + + + + + + + + + + + + C + + + + + + + + + + + + D − − − − − − − − −− − − E − − − − − − − − − − − − F + + + + + + + + + + + +G + + + + + + + + + + + + H + + + + + + + + + + + +

Data from the sequencing runs was analyzed and is presented in Table 9.Table 9 summarizes the performance parameters for each run, showing thenumber of multiplexed samples, whether the samples were identified aspositive or negative for Salmonella, the number of active nanoporesequencing pores available in each run, the number of total readsgenerated for each run, and the number of false positive (FP) or falsenegative (FN) calls for Salmonella presence in each run.

Table 9 illustrates sample classification as positive or negative forSalmonella and Performance of Nanopore Sequencing for each of 3successive runs on the same flow cell.

TABLE 9 Run Total Index Flow Active Total Id samples plate PositivesNegatives cell pores reads FP FN 1 96 IP1 72 24 New 1485 1.85M 0 0 2 96IP2 48 48 Run1 washed 1104 1.22M 0 0 3 96 IP3 72 24 Run2 washed 8651.03M 0 0

Surprisingly, high numbers of reads (1.03-1.85 million) were generatedfor each run (well above the minimum acceptable minimum threshold of 10Kreads per sample). Additionally, the data from each run allowed for 100%accuracy in correctly calling the samples as positive or negative forSalmonella presence (e.g. zero false positive or false negative calls)and the accuracy in calls did not decline between runs.

The results in Table 9 thus demonstrate that, unexpectedly, the claimedmethod is capable of correctly distinguishing as many as 96uniquely-barcoded samples stacked/multiplexed together in a singlesequencing run on a nanopore flow cell, and that this can be repeated onthe same nanopore flow cell as many as 3 times with no functionaldecline in data quality.

While preferred embodiments of the present invention have been shown anddescribed herein, such embodiments are provided by way of example only.It is not intended that the invention be limited by the specificexamples provided within the specification. While the invention has beendescribed with reference to the aforementioned specification, thedescriptions and illustrations of the embodiments herein are not meantto be construed in a limiting sense. Numerous variations, changes, andsubstitutions will now occur to those skilled in the art withoutdeparting from the invention. Furthermore, it shall be understood thatall aspects of the invention are not limited to the specific depictions,configurations or relative proportions set forth herein which dependupon a variety of conditions and variables. It should be understood thatvarious alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is thereforecontemplated that the invention shall also cover any such alternatives,modifications, variations or equivalents. It is intended that thefollowing claims define the scope of the invention and that methods andstructures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A library preparation system comprising: (a) aplatform comprising a plurality of containers, wherein each container ofa first subset of said plurality of containers comprises a sample,wherein said sample comprises an isolated nucleic acid sequence, andeach container of a second subset of said plurality of containerscomprises a plurality of reagents; (b) a fluid handling systemcomprising a robotic arm configured to withdraw and dispense (i) saidsample from said first subset of said plurality of containers or (ii)said plurality of reagents from said second subset of said plurality ofcontainers; (c) an automated translation stage disposed above saidplatform and coupled to said fluid handling system; and (d) a controllercommunicatively coupled to said fluid handling system and said automatedtranslation stage.
 2. The library preparation system of claim 1, furthercomprising: a library preparation chamber comprising a heating elementand a thermometer, and wherein said heating element is configured tocycle a temperature of said platform between a first temperature and asecond temperature.
 3. The library preparation system of claim 1,further comprising a sequencing flow cell that is configured for a poresequencing reaction.
 4. The library preparation system of claim 1,further comprising a sequencing flow cell that is configured for asequencing-by-synthesis reaction.
 5. The library preparation system ofclaim 1, further comprising a sequencing chamber comprising a sequencingflow cell, wherein said robotic arm is configured to move said mixtureto said sequencing chamber.
 6. The library preparation system of claim5, wherein said sequencing chamber is disposed on said platform.
 7. Thelibrary preparation system of claim 1, wherein said fluid handlingsystem further comprises one or more pumps or compressors.
 8. Thelibrary preparation system of claim 2, wherein the controller comprisesinstructions configured to implement a method, wherein said instructionscomprise cycling, by said heating element, said temperature of saidplatform between said first temperature and said second temperature inan amplification reaction of at least a portion of said isolated nucleicacid sequence.
 9. The library preparation system of claim 1, wherein thecontroller comprises instructions, wherein said instructions furthercomprise barcoding said isolated nucleic acid sequence by coupling saidone or more barcode nucleic acid sequences to at least a portion of saidisolated nucleic acid sequence.
 10. The library preparation system ofclaim 1, further comprising a mechanical mixer comprising a motormechanically coupled to said plurality of containers, wherein saidmechanical mixer is configured to agitate said plurality of containers.11. The library preparation system of claim 1, wherein the controllercomprises instructions configured to implement a method, wherein saidinstructions comprise activating said mechanical mixer to agitate saidplurality of containers, thereby mixing said sample with said pluralityof reagents to produce said mixture.
 12. The library preparation systemof claim 1, wherein said plurality of reagents further comprises anucleic acid primer.
 13. The library preparation system of claim 2,wherein said instructions further comprise: sensing a third temperatureproximal to said platform that is below a set temperature; turning onsaid heating element; sensing a fourth temperature proximal to saidplatform that is said set temperature; and turning off said heatingelement.
 14. The library preparation system of claim 1, furthercomprising an antifouling coating coupled to an interior surface of saidfluid handling system.
 15. The library preparation system of claim 1,further comprising a gas cylinder comprising a gas, wherein said gascylinder is fluidically connected with said fluid handling system, andwherein said fluid handling system is configured to spray said firstsubset of said plurality of containers with said gas.
 16. The librarypreparation system of claim 1, wherein said second subset of saidplurality of containers comprises (i) a first container comprising saida first barcode nucleic acid sequence of said one or more barcodenucleic acid sequences, and (ii) a second container comprising a secondbarcode nucleic acid sequence of said one or more barcode nucleic acidsequences, wherein said first barcode nucleic acid sequence is differentthan said second barcode nucleic acid sequence.
 17. The librarypreparation system of claim 16, wherein said one or more barcode nucleicacid sequences in each container of said second subset of said pluralityof containers is different than said one or more barcode nucleic acidsequences in another container of said second subset of said pluralityof containers.
 18. The library preparation system of claim 16, whereineach container of said second subset of said plurality of containerscomprises two or more types of said one or more barcode nucleic acidsequences.
 19. The library preparation system of claim 16, wherein saidone or more or more barcode nucleic acid sequences comprises at leastthree distinct barcode nucleic acid sequences comprising a first barcodenucleic acid sequence, a second barcode nucleic acid sequence, and athird barcode nucleic acid sequence, and wherein said combining saidsample with said plurality of reagents to produce said mixturecomprises: adding said first barcode nucleic acid sequence to a firstplurality of said isolated nucleic acid sequence in said sample, therebyproviding a first plurality of indexed nucleic acid sequences; adding asecond barcode nucleic acid sequence to said first plurality of saidisolated nucleic acid sequence in said sample, thereby providing asecond plurality of indexed nucleic acid sequences; and adding a thirdbarcode nucleic acid sequence to said first plurality of said isolatednucleic acid sequence in said sample, thereby providing a thirdplurality of indexed nucleic acid sequences.
 20. The library preparationsystem of claim 19, wherein said instructions further comprise:performing, by said sequencing chamber, a sequencing reaction on saidthird plurality of said isolated nucleic acid sequence; anddemultiplexing, by said controller, said third plurality of saidisolated nucleic acid sequence comprising said first barcode nucleicacid sequence, said second barcode nucleic acid sequence, and said thirdbarcode nucleic acid sequence.
 21. A method of preparing a librarycomprising: inserting a first sample comprising a nucleic acid into alibrary preparation chamber, wherein the first sample is comprisedwithin a container; mixing the first sample with a first reagent using afluid handling system; withdrawing the first sample from the containerusing the fluid handling system; and inserting the first sample into aflow cell.
 22. The method of claim 21, wherein the inserting isperformed under conditions sufficient to prevent failure of the flowcell due to introduction of air into the flow cell.
 23. The method ofclaim 21, wherein the conditions sufficient to prevent failure of theflow cell due to introduction of air into the flow cell comprise notintroducing air bubbles into a flow path into the flow cell.
 24. Themethod of claim 21, further comprising introducing, by the fluidhandling system, a conditioning liquid into the flow cell to removeobstructions in the flow path of the flow cell.
 25. The method of claim24, further comprising introducing, by the fluid handling system, abuffer solution into the flow cell to displace the conditioning liquidin the flow path of the flow cell.
 26. The method of claim 25, furthercomprising creating a continuous fluid channel in the flow path of theflow cell sufficient to prevent the failure of the flow cell due to theintroduction of air into the flow path of the flow cell.
 27. The methodof claim 26, further comprising flushing the flow cell with a fluiddispensed from the fluid handling system following inserting the sampleinto the flow cell.
 28. The method of claim 23, wherein inserting thefirst sample into a flow cell comprises dispensing, by the fluidhandling system, a 3 microliter to 50 microliter volume of the sampleinto a sample input port of the flow cell.
 29. The method of claim 28,further comprising contacting the sample input port with the fluidhandling system prior to inserting the sample into the sample input portof the flow cell.
 30. The method of claim 21, further comprising addingat least one barcoded nucleic acid sequence to the sample and mixing thesample prior to inserting the first sample into the flow cell.